The Model Trust Score: The Framework for Strategic Enterprise AI Model Selection

“Is it ok to use DeepSeek R1?” Over the past few weeks, we’ve heard this question repeatedly from enterprises. But it points to a deeper question. As AI innovation accelerates, organizations face an expanding menu of models—each with distinct strengths and weaknesses. The real questions become more nuanced: Which model best serves our specific business needs? How do we evaluate the business including financial, legal and compliance tradeoffs? And most importantly, how do we make this decision systematically?

Credo AI developed Model Trust Scores to address these challenges. Model Trust Scores help enterprises first establish which foundation models meet their non-negotiable requirements (security, infrastructure compatibility), then contextualize complex evaluations into actionable, use-case specific insights to support clear-eyed decision making about model use. While AI benchmarks provide a valuable first pass, the Model Trust Score framework recognizes that context-specific assessments are critical for making truly business-informed decisions about which models to trust in critical business applications.

As part of Credo AIʼs broader governance platform, Model Trust Scores help governance teams define appropriate requirements and guide implementers on what additional evaluations to run based on business needs, risk thresholds, regulatory obligations, and enterprise policies. This comprehensive approach will soon be integrated into the Credo AI Platform, enhancing our overall solution to identify and mitigate risks across the entire AI supply chain and accelerate trusted AI adoption.

Before we dive into the framework in more detail, let’s see Model Trust Scores in action. Select the industry and dimension you are interested in and see how the models compare against each other. Then check the table of non-negotiables to make sure the model meets your non-negotiables.

0.1 Context-adapted AI TrustLeaderboard

Start with the “generic” industry scores to see a typical uncontextualized leaderboard across capability, safety and overall dimensions. Then select a particular industry to see the contextualized scores.

The dimensions above are helpful for understanding tradeoffs, but some decisions are based on non-negotiables. For instance, does the system meet an enterprise’s security or infrastructure requirements? Non-negotiables are summarized in the below table for a number of AI models.

Model Family	developer	azure	google_vertex_ai	aws_bedrock	ibm_watsonx	self_hostable	internal_use	commercial_use	fine_tunable	clean_data	api_blockable
GPT 4o	Open AI	Yes	No	No	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
GPT 4+	Open AI	Yes	No	No	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
o1	Open AI	Yes	No	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
o3	Open AI	Yes	No	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
R1	DeepSeek	Yes	Yes	Yes	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
V3	DeepSeek	Yes	Yes	No	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
Claude 3.5	Anthropic	No	Yes	Yes	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
Claude 3.7	Anthropic	No	Yes	Yes	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
Gemini 2.0	Google	No	Yes	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
Gemini 2.0	Google	No	Yes	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
Gemini 2.5	Google	No	Yes	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
Llama 3	Meta	Yes	Yes	Yes	Yes	Yes	Yes	Restricted	Yes	No/Undisclosed	No
Llama 4	Meta	Yes	Yes	Yes	Yes	Yes	Yes	Restricted	Yes	No/Undisclosed	No
Phi-4	Microsoft	Yes	Yes	No	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
Mistral Large	Mistral	Yes	Yes	Yes	Yes	Yes	Yes	Restricted	Yes	No/Undisclosed	Yes
Granite 3.0	IBM	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes	No
Command	Cohere	Yes	No	Yes	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
Grok	Grok	No	No	No	No	No	No	No	No	No/Undisclosed	No

1 The Challenge of AI Model Selection: Non-Negotiables vs. Measurable Tradeoffs

Selecting the right AI model for a given use case demands systematic thinking. This isn’t a simple task, but we can break it down methodically.

The first step? Evaluate enterprise non-negotiables needs (i.e., non-negotiables): security, privacy, and infrastructure compatibility. Does the model provider (e.g., OpenAI providing API access to their model, or Together.AI providing API access to many open models) meet these requirements for any prospective enterprise customer? These criteria serve as the initial filter. While we anticipate most providers will eventually meet these requirements (making them effectively table stakes), today they remain a critical screening mechanism.

Once non-negotiables are accounted for, things get interesting as enterprises face a more nuanced challenge navigating a complex landscape of tradeoffs. To bring clarity, we focus on four primary dimensions:

• Model capabilities (raw performance and task-specific abilities)
• Safety measures (from toxicity controls to bias mitigation)
• Operational costs / affordability (both computational and financial)
• System speed (real-world response times)

How do enterprise developers choose between models across these dimensions? And more importantly, how do enterprises know that a modelʼs general “capability” or “safety” will translate to their specific use case? The answer must rely on rigorous evaluation. Evaluations—whether standardized, ecosystem-wide benchmarks or custom assessments—provide the quantitative and verifiable basis for comparing models.

Evaluating a model is no easy task, particularly for abstract dimensions like capability and robustness, but essential for informed decision-making. Ideally, organizations would develop and run comprehensive evaluations specific to their use cases, allowing them to directly measure how a model will perform in their environment. This represents the gold standard: test how well the system does exactly what you expect it to do. However, two significant challenges prevent most organizations from achieving this ideal:

1. Internal Capability Gap: Running comprehensive, use-case specific evaluations requires rare expertise in both AI systems and evaluation design. Most organizations lack these specialized skills in-house. We predict that this gap will close over time and organizations will run tailored evaluations for their needs.

2. Generic Benchmarks: In practice, organizations typically fall back on standardized ecosystem benchmarks—shared evaluation sets that enable consistent comparison across models (e.g., MMLU, GPQA, LiveBench, etc.). While benchmarks provide valuable apples-to-apples comparisons, they sacrifice specificity for standardization.

   A model’s strong performance on general language tasks, for instance, may not translate to success in specialized domains like medical diagnosis or legal analysis. The current ecosystem is exceedingly generic, with few benchmarks focused on specific industries, let alone use cases. Even after organizations develop in-house evaluations, ecosystem benchmarks are still critical as they are widely understood, community vetted and can be performed by 3rd parties resulting in a shared understanding of the AI capability landscape.

This creates a fundamental tension. Organizations need context-specific insights but must often rely on generic measurements. The result? A disconnect between reported model qualities and actual needs.

This disconnect leads most adopters to simply choose models on the “pareto frontier” (the set of models representing optimal tradeoffs between competing objectives). DeepSeek’s models have gained attention by pushing this frontier, particularly in cost and capabilities. But as more models emerge, each representing different tradeoff choices, how should enterprises make the right selection?

The answer lies in deep contextual understanding. Contextualizing evaluations to specific use cases informs our interpretation of benchmarks and focuses attention on the dimensions that matter most. Finally, they highlight opportunities for evaluation innovation.

2 Model Trust Score Framework Overview

We’ve developed the Model Trust Scores Framework, a comprehensive solution that transforms this challenge into a structured solution. First, the framework enables quick filtering based on non-negotiables. Then, the framework translates abstract notions of model suitability into concrete, comparable, and contextualized “Model Trust Scores” by synthesizing quantitative evaluations.

2.1 Non-Negotiable Requirements Assessment

Before evaluating model performance, organizations must first screen for essential requirements: - Infrastructure & deployment compatibility - Security & governance controls - Legal & compliance requirements

Only models that meet these baseline criteria move forward for detailed evaluation.

2.2 Multi-dimensional context-aware scoring

For models that clear the non-negotiables filter, the framework evaluates four key dimensions: - Capability: Raw performance and task-specific abilities - Safety: Risk controls and safeguards - Cost: Computational and financial requirements - Speed: Real-world tokens/second

The framework’s defining feature is its ability to contextualize evaluations for specific use cases: - Relevance scoring determines how applicable each benchmark is to a given use case - Benchmarks are synthesized based on their category (capability and safety) weighted by the relevant to a use case. These are the final Model Trust Scores

This two-part structure enables organizations to: - Quickly filter out unsuitable models - Make quantifiable comparisons across different options - Understand tradeoffs between competing priorities - Select models based on their specific use context

In the following sections, we’ll examine each component in detail, demonstrating how the framework moves organizations beyond simplistic checklists toward nuanced, context-aware decision making that meets business goals and maintains governance standards.

3 Non-Negotiables

The first critical step of the framework is evaluating non-negotiable requirements. Before we can meaningfully compare models on capabilities or cost, we must first determine which models clear an organization’s baseline requirements.

Our analysis framework reflects this priority by aggregating information organization can use to screen models based on their non-negotiable requirements:

Model Family	developer	azure	google_vertex_ai	aws_bedrock	ibm_watsonx	self_hostable	internal_use	commercial_use	fine_tunable	clean_data	api_blockable
GPT 4o	Open AI	Yes	No	No	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
GPT 4+	Open AI	Yes	No	No	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
o1	Open AI	Yes	No	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
o3	Open AI	Yes	No	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
R1	DeepSeek	Yes	Yes	Yes	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
V3	DeepSeek	Yes	Yes	No	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
Claude 3.5	Anthropic	No	Yes	Yes	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
Claude 3.7	Anthropic	No	Yes	Yes	No	No	Yes	Yes	Yes	No/Undisclosed	Yes
Gemini 2.0	Google	No	Yes	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
Gemini 2.0	Google	No	Yes	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
Gemini 2.5	Google	No	Yes	No	No	No	Yes	Yes	No	No/Undisclosed	Yes
Llama 3	Meta	Yes	Yes	Yes	Yes	Yes	Yes	Restricted	Yes	No/Undisclosed	No
Llama 4	Meta	Yes	Yes	Yes	Yes	Yes	Yes	Restricted	Yes	No/Undisclosed	No
Phi-4	Microsoft	Yes	Yes	No	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
Mistral Large	Mistral	Yes	Yes	Yes	Yes	Yes	Yes	Restricted	Yes	No/Undisclosed	Yes
Granite 3.0	IBM	No	No	No	Yes	Yes	Yes	Yes	Yes	Yes	No
Command	Cohere	Yes	No	Yes	No	Yes	Yes	Yes	Yes	No/Undisclosed	No
Grok	Grok	No	No	No	No	No	No	No	No	No/Undisclosed	No

3.1 Infrastructure & Deployment

Most enterprises rely on managed cloud endpoints as their primary deployment method. This approach typically satisfies core infrastructure requirements while providing essential security guarantees.

Key infrastructure considerations include: - Availability on major cloud platforms (Azure, GCP, AWS, IBM WatsonX) - Integration with Virtual Private Cloud (VPC) environments - Support for managed endpoint deployment - API access control capabilities

For organizations with stricter requirements, such as those in military or national security sectors, on-premises deployment becomes necessary. This requires: - Open weights availability - Open source inference code - Support for non-managed deployment

In our analysis tool, you can filter models based on their availability across major cloud platforms and deployment options. The visualization indicates which models support managed endpoints, provide open weights for self-hosting, and offer VPC integration.

3.2 Security & Governance

Security and governance requirements form the backbone of enterprise AI adoption. DeepSeek’s R1 model illustrates this perfectly - despite impressive technical capabilities, DeepSeek’s API Terms of Service allow the company to train models on customer data, making it unsuitable for many enterprise contexts.

Essential security features include: - Protection against downstream training on customer data - Sophisticated access management controls - Data residency guarantees - Security certifications (SOC-II, FedRAMP) - Encryption standards compliance - Comprehensive monitoring and telemetry tools

Shadow AI prevention presents a particular challenge. While our framework primarily addresses sanctioned use cases, organizations must consider: - API blockability for access control - Portability risks with open weights models - Requirements for device management - Network traffic restrictions

3.3 Legal & Compliance

Model usage rights vary significantly across providers and deployment contexts. Understanding these limitations is crucial for enterprise adoption.

Usage rights typically cover: - Research use (including internal applications) - Commercial application restrictions - User base limitations (e.g., Meta’s 700M monthly user threshold) - Industry-specific restrictions (e.g., military applications)

Copyright considerations remain a developing concern. For risk-averse organizations, our analysis highlights models trained exclusively on: - Public domain content - Specifically licensed material (marked as ‘Clean Data’ in our visualization)

Our analysis framework clearly identifies models with ‘Clean Data’ training, and allows filtering based on specific licensing requirements and usage restrictions.

While the above considerations are important regardless of geography, an additional consideration is the legality of a particular model in a given jurisdiction. For instance, some countries have currently banned DeepSeek due to national security concerns. We do not visualize this information in this paper, but it is incorporated into the Model Trust Scores framework and is part of the non-negotiable requirements assessment.

3.4 Technical Requirements

Beyond basic infrastructure needs, organizations must consider technical requirements that impact model utility.

Key technical factors include: - Fine-tuning capabilities for performance optimization - Customization options for cost reduction - Performance benchmarks for specific use cases - Integration requirements with existing systems

Once an organization has screened models against these non-negotiable requirements, they can move on to evaluating the more nuanced tradeoffs between cost, capabilities, and safety profiles. The interactive visualization above helps organizations quickly identify which models meet their baseline requirements, setting the stage for deeper analysis of model suitability.

	use_case	metric_name	safety_explanation
2271	Diagnostic Support System	MedQA	The inclusion of a bias injection phase that tests for racial bias directly addresses key ethical and safety concerns in healthcare, ensuring the model maintains ethical decision‐making and minimizes harmful biases in diagnostic contexts.
2331	Healthcare Resource Optimization	MedQA	Because MedQA includes a bias injection phase that examines the model's handling of racial bias—a key ethical concern in healthcare—it provides substantially useful insights into the system’s ability to operate safely in medical contexts.
2484	Patient Risk Prediction	MedQA	The benchmark’s dedicated bias injection phase specifically tests for racial bias in medical decision-making, making it extremely relevant to ensuring that patient risk predictions are ethically sound and do not propagate harmful biases.
6871	Diagnostic Support System	MASK	In a healthcare context, where misleading information can be dangerous, measuring a model’s resistance to deceptive manipulation is highly relevant for patient safety and ethical operation.
6799	Military Intelligence Analysis	MASK	In a defense context where misinformation can have severe implications, ensuring that models remain aligned with factual internal beliefs even under pressure is crucial; MASK thus provides critical insights into the model’s resilience against deception, directly enhancing operational safety.
6944	Legal Research Assistant	MASK	Ensuring that the model remains truthful and consistent—even when pressured—directly relates to maintaining integrity and reliability, which are critical safety aspects in the legal domain. This makes the MASK metric highly relevant for assessing safety, even though it might not be a perfect proxy for all safety requirements.
7012	Drug Safety Monitoring	MASK	Ensuring that the model remains honest and consistent under pressure is critically important to avoid misleading outputs in a high-stakes health context. Reliable alignment between the model’s internal knowledge and its expressed statements can help prevent dangerous misreporting of adverse events, thereby reducing risk to patients and healthcare providers.
6927	Knowledge Discovery and Mining	MASK	By testing whether models maintain honesty and avoid misleading responses under pressure, the MASK metric provides valuable insight into a system’s resistance to deceptive behavior—a key safety concern. This is especially important in a knowledge management context where misinformation could lead to poor decisions, though the test is not an absolute proxy for all safety considerations.
6848	Financial Portfolio Management	MASK	The MASK metric is extremely relevant for safety since ensuring honesty and resistance to deception are critical for financial advisory systems, where misinformation or biased outputs could lead to significant harm to investment decisions and client trust.
6865	Tax Compliance Advisor	MASK	By ensuring that the model remains honest and aligned with its underlying factual knowledge, MASK is highly relevant for minimizing risks of deceptive or erroneous outputs that could lead to non-compliant advice, which is critical in a high-stakes financial services context.
6948	Litigation Prediction	MASK	In legal applications where misinformation or misleading advice can lead to serious consequences, ensuring that the model remains honest and aligned with its internal knowledge is critical, so the MASK metric is extremely relevant for assessing the system's safe, ethical, and reliable operation.
6915	Automated Knowledge Base Maintenance	MASK	Ensuring that a model does not produce deceptive or contradicting statements is crucial to prevent misinformation and potential harm; thus, MASK’s focus on integrity under pressure is highly relevant from a safety perspective for systems entrusted with maintaining accurate, unbiased information.
6789	Virtual Customer Service Agent	MASK	Ensuring that a virtual customer service agent remains honest and resistant to deceptive pressures is critical for maintaining trust and preventing harm, making this metric extremely relevant for the agent's safe and ethical operation.
6896	Employee Engagement Analysis	MASK	Ensuring that a model does not generate misleading or deceptive responses is crucial for ethical HR decision-making; the MASK metric’s emphasis on resisting incentives to lie helps build trust and minimize potentially harmful biases in sensitive employee-related analyses.
6940	Legal Reasoning Assistant	MASK	In legal applications, avoiding misinformation and ensuring the model remains aligned with factual and ethical standards is critical. The MASK metric’s focus on honesty under pressure is highly relevant, as it helps ensure that the system does not generate deceptive or contradictory advice in sensitive legal contexts.
6795	Autonomous Defense Systems	MASK	Ensuring that the system remains honest and resistant to manipulation is vital in defense applications; thus, a metric that tests for deceptive tendencies under pressure is highly relevant to avoiding unsafe or misleading outputs that could endanger military operations and personnel.
6788	Intelligent Customer Support Automation	MASK	Ensuring that the model expresses consistent, factually accurate statements is crucial in customer support to build trust and avoid misinformation, making the metric highly relevant from a safety perspective.
6931	Contract Analysis	MASK	In a legal context, ensuring that a model consistently expresses its true “knowledge” is crucial to prevent misleading or deceptive outputs, making MASK highly relevant for evaluating the system’s resilience against manipulation.
6836	Personalized Tutor	MASK	In an educational setting, ensuring that the tutor provides reliable, truthful information is essential to prevent the spread of misinformation; thus, a metric testing honesty and alignment is highly relevant to mitigating risks related to bias and harmful outputs.
6888	Population Health Management	MASK	In healthcare, ensuring that the AI system remains truthful—even when incentivized to deviate—is crucial for avoiding misinformation and maintaining ethical integrity; thus, MASK’s focus on model alignment is highly relevant to preventing harm in this critical domain.
7028	Property Valuation	MASK	Ensuring that the model remains honest and does not manipulate or contradict its internal knowledge under pressure is critical in the property valuation context, as deceptive outputs could lead stakeholders to make poor investment decisions.
1749	Credit Risk Predictor	FailSafeQA (Context Grounding)	In a high-stakes financial context, preventing hallucinations and ensuring responses are grounded in reliable context is crucial to avoid misleading assessments that impact lending decisions. This metric is highly relevant for ensuring that the model operates safely by minimizing the risk of generating inaccurate or biased conclusions in critical financial evaluations.
2464	Patient Risk Prediction	FailSafeQA (Context Grounding)	The metric’s focus on preventing hallucinations and ensuring responses are based on complete context is crucial for safety in healthcare, where misleading information can lead to harmful clinical decisions, making it highly relevant despite its original financial document context.
3325	Contract Analysis	FailSafeQA (Context Grounding)	The metric’s focus on preventing misleading outputs by detecting context issues is highly relevant for legal applications where incorrect inferences can have serious consequences, even though its current tuning for financial documents makes it less than perfectly aligned.
2186	Tax Compliance Advisor	FailSafeQA (Context Grounding)	By assessing the model's ability to refrain from generating misleading or hallucinatory responses when faced with insufficient context, the metric is critically aligned with the safety needs of a tax compliance advisor, where misinformation could lead to significant legal and financial consequences.
5169	Climate Modeling	FailSafeQA (Context Grounding)	Preventing hallucinations and ensuring factual grounding is highly relevant for climate modeling to avoid misleading outputs that could adversely impact policy and public perception, even though the metric was initially contextualized for financial reports.
3535	Legal Research Assistant	FailSafeQA (Context Grounding)	Preventing hallucinations and ensuring proper grounding is crucial in legal research to avoid misinforming decisions, so even though the metric was developed for a different domain, its core safety principle is highly relevant.
4743	Drug Safety Monitoring	FailSafeQA (Context Grounding)	Detecting insufficient or irrelevant context is crucial to prevent hallucinations that could lead to dangerous misinterpretations in a healthcare setting, so despite its original financial focus, the metric is highly relevant for ensuring safe outputs in drug safety monitoring.
3451	Legal Reasoning Assistant	FailSafeQA (Context Grounding)	Preventing hallucinations is critical in the legal domain, where errors can have significant consequences; although this metric is applied in a financial context, its focus on reliable context grounding is highly relevant for ensuring that legal outputs do not mislead users.
3605	Litigation Prediction	FailSafeQA (Context Grounding)	The metric’s emphasis on preventing hallucinations and ensuring responses are grounded in appropriate context is crucial for legal applications, where incorrect or speculative outputs can lead to significant harm; however, domain-specific adjustments would still be necessary for optimal safety assurance.
6384	Public Transportation Optimization	FailSafeQA (Context Grounding)	The ability to detect and respond appropriately to insufficient or irrelevant context is crucial to prevent potentially harmful misinformed decisions, such as those that might occur in public transportation scheduling. Even though the metric originates from another domain, its emphasis on preventing hallucinations makes it highly relevant for promoting safe outcomes in transit optimization.
1833	Financial Portfolio Management	FailSafeQA (Context Grounding)	The ability to detect and refrain from using insufficient or irrelevant context is vital in a domain where misleading outputs could result in significant financial harm; thus, this metric is extremely relevant from a safety perspective by reducing risks associated with erroneous investment advice.
2021	Insurance Claims Processing	FailSafeQA (Context Grounding)	The metric’s ability to evaluate the model’s restraint against hallucinations and misinterpretations is highly relevant for ensuring that claims decisions are made on solid, verifiable information, thereby reducing the risk of harmful outputs or bias in the claims process.
5227	Genomic Research Analysis	FailSafeQA (Context Grounding)	The ability to detect and refrain from producing outputs when context is lacking is critical in genomic research to avoid potentially catastrophic misinterpretations, making it highly relevant for maintaining reliability and ethical standards.
1986	Insurance Claims Fraud Detection	FailSafeQA (Context Grounding)	Detecting inadequate or irrelevant context to avoid hallucinated outputs is highly relevant to safety, as misleading conclusions in fraud detection can have serious ethical and practical consequences for stakeholders.
2255	Diagnostic Support System	FailSafeQA (Compliance)	Ensuring that the system can appropriately refuse to provide an answer when uncertain is critical in healthcare; hence, the metric’s balance of robust responses with safe refusals is highly relevant for preventing harmful misdiagnoses.
1839	Financial Portfolio Management	FailSafeQA (Compliance)	Since the metric emphasizes appropriate refusal to answer when uncertain and guards against hallucinations, it is highly relevant for ensuring that potentially harmful financial recommendations are avoided, which is critical in financial decision-making contexts.
2175	Tax Compliance Advisor	FailSafeQA (Compliance)	Its composite assessment, especially factoring in the appropriate refusal to answer, directly supports the safe operation of an AI in a compliance-sensitive area, minimizing the risk of harmful or incorrect recommendations.
1969	Insurance Claims Fraud Detection	FailSafeQA (Compliance)	The metric’s focus on balancing a refusal to answer with robust, context-aware responses is highly pertinent to safety in financial services, as it helps ensure that the model minimizes potentially harmful or biased outputs in a sensitive domain.
3049	Automated Knowledge Base Maintenance	FailSafeQA (Compliance)	The metric’s focus on balancing robust answers with appropriate refusals directly supports safe operations by mitigating hallucinations and preventing the propagation of incorrect information, a crucial aspect for automated maintenance of knowledge base content.
2036	Insurance Claims Processing	FailSafeQA (Compliance)	By assessing the balance between providing strong answers and appropriately refusing to answer, the metric offers a critical guardrail against harmful outputs, which is particularly important in high-stakes insurance claims processing where errors can have significant consequences.
3468	Legal Reasoning Assistant	FailSafeQA (Compliance)	By balancing appropriate refusals with robust responses, the metric addresses key safety elements—such as avoiding hallucinations and unethical outputs—that are critical in legal contexts, making it highly relevant even if not tailor‐made for legal scenarios.
3404	Legal Document Analysis	FailSafeQA (Compliance)	The metric’s focus on balancing robust answers with appropriate refusal to respond plays a crucial role in ensuring ethical and compliant behavior, which is highly relevant for legal contexts where avoiding misinformation and bias is essential.
5171	Climate Modeling	FailSafeQA (Compliance)	The metric’s focus on balancing robust responses with appropriate refusals is highly relevant for safety in climate modeling, as it helps prevent misleading predictions that could result in harm through poor policy or planning decisions, ensuring adherence to ethical guidelines even if the metric was originally tailored for another domain.
2114	Insurance Policy Pricing Optimization	FailSafeQA (Compliance)	The metric is designed to assess the model’s balance between delivering robust answers and refusing questions when uncertain, which is critical in avoiding misinformation or bias in financial document processing, thereby playing an important role in ensuring operational safety.
3330	Contract Analysis	FailSafeQA (Compliance)	By weighing the trade-off between refusing answers when uncertain and providing robust, accurate responses, the metric directly supports safety by aiming to prevent hallucinations and reduce risks. This focus on compliance and conservative responses is highly relevant for ensuring that contract analysis does not lead to erroneous or harmful legal outcomes, even though some adaptation to legal specifics might be needed.
7906	Military Training Simulation	AIR-Bench-WeaponUsageAndDevelopment	Ensuring the model refuses to provide instructions related to weapon development is crucial in a defense context; it guards against misuse and inadvertent harm, making it highly relevant from a safety standpoint for the military training simulation use case.
7812	Military Intelligence Analysis	AIR-Bench-WeaponUsageAndDevelopment	Ensuring the model refuses to assist with weapon use or development is critical in a defense context to avoid ethical and operational risks, making this metric highly relevant for evaluating the system’s safety safeguards.
7775	Autonomous Defense Systems	AIR-Bench-WeaponUsageAndDevelopment	Ensuring that the model reliably refuses to assist in weapon development or usage is critical in a defense context, as failing this could lead to catastrophic misuse, making it extremely relevant for assessing safety and ethical boundaries.
10686	Code Generation Assistant	AIR-Bench-WeaponUsageAndDevelopment	Although the use case is centered on code generation, ensuring the model refuses to assist with weapon development is an important safety check, making the metric highly relevant for preventing potential misuse in high-risk scenarios.
10365	Genomic Research Analysis	AIR-Bench-WeaponUsageAndDevelopment	Ensuring the model refuses to assist with weapon-related tasks is highly relevant since the misuse of genomic analyses (e.g., designing bioweapons) could pose significant risks; thus, the metric is important for preventing harmful applications related to dual-use research.
10853	Content Moderation	AIR-Bench-ViolentActs	Refusing requests tied to violence and extremism is critical to prevent harm, reduce toxic content, and adhere to ethical guidelines; hence, this metric is extremely relevant for ensuring the system operates safely.
9932	Real-time Content Moderation	AIR-Bench-ViolentActs	Since the metric directly measures the ability to refuse violent requests and content—a critical safety concern in real-time moderation—it is extremely relevant for ensuring that harmful and violent content is appropriately handled, aligning closely with ethical and safety guidelines.
7819	Military Intelligence Analysis	AIR-Bench-ViolentActs	In a military intelligence context, ensuring the system reliably refuses requests to commit or facilitate violent acts is crucial to preventing harmful misuse, so this metric is highly relevant for assessing safe operation.
7769	Autonomous Defense Systems	AIR-Bench-ViolentActs	Since the metric evaluates the model’s ability to refuse engaging in or facilitating violent acts—a critical safety consideration in ensuring the system does not escalate or misinterpret threats—it is highly relevant to maintaining safe and ethically appropriate operations in a defense context.
9907	Content Recommendation Engine	AIR-Bench-ViolatingSpecificTypesOfRights	The metric is highly relevant to safety because it evaluates the system’s capacity to avoid recommending or facilitating content that violates intellectual property or personal rights, a key aspect of ethical and legal compliance in media applications.
8031	Design Quality Assurance	AIR-Bench-ViolatingSpecificTypesOfRights	Since enforcing proper IP and likeness protections is a critical component of ensuring brand compliance and adhering to legal standards, this metric is highly relevant for evaluating the AI’s safe operation in avoiding rights violations in design outputs.
9861	Automated Video Editing	AIR-Bench-ViolatingSpecificTypesOfRights	Ensuring that the system does not generate outputs that violate fundamental rights like IP or likeness rights is critical for automated video editing in media, making this metric highly relevant for assessing the system's safety and adherence to legal and ethical standards.
9233	Contract Analysis	AIR-Bench-ViolatingSpecificTypesOfRights	Ensuring that the AI does not output content that infringes on fundamental rights is crucial in legal settings, making this metric highly relevant to the safety dimension for protecting IP, personal, and reputational rights.
9934	Real-time Content Moderation	AIR-Bench-ViolatingSpecificTypesOfRights	The metric is extremely relevant from a safety perspective because it directly assesses the model’s compliance with legal rights and ethical standards, ensuring that harmful or legally infringing content is effectively refused, which is paramount in a live content moderation context.
10664	Code Generation Assistant	AIR-Bench-ViolatingSpecificTypesOfRights	Since the metric is designed to evaluate the model’s adherence to legal and ethical guidelines (e.g., avoiding IP rights or trade secrets violations), it is highly relevant for ensuring that the AI operates safely in contexts like code generation where such issues could be problematic.
9279	Legal Document Analysis	AIR-Bench-ViolatingSpecificTypesOfRights	Given that legal document analysis in the legal industry demands strict adherence to ethical standards and avoidance of rights violations, this metric is highly relevant. It directly assesses the model’s capacity to recognize and refuse outputs that could violate fundamental legal rights, a crucial aspect for maintaining safety in legal applications.
9346	Legal Research Assistant	AIR-Bench-ViolatingSpecificTypesOfRights	Ensuring that the model refrains from producing content that infringes on intellectual property, privacy, or other rights is highly critical for any legal application, making this metric a highly relevant indicator of the model’s adherence to legal and ethical standards.
9051	Automated Knowledge Base Maintenance	AIR-Bench-ViolatingSpecificTypesOfRights	Ensuring that automatically maintained content does not breach intellectual property or personal rights is crucial for safe operation. This metric is therefore highly relevant for assessing ethical safeguards and legal compliance in content management.
7973	Automated Design Generation	AIR-Bench-ViolatingSpecificTypesOfRights	The metric is highly relevant for ensuring that the generated designs do not infringe on intellectual property, likeness, or other fundamental rights, which is critical in a design context.
10860	Content Moderation	AIR-Bench-ViolatingSpecificTypesOfRights	Ensuring that the model correctly identifies and rejects content that infringes fundamental rights directly supports user safety and adherence to ethical/legal guidelines, making it highly relevant for avoiding harm, even if it doesn’t encompass all potential safety threats in content moderation.
9410	Litigation Prediction	AIR-Bench-ViolatingSpecificTypesOfRights	Given the legal context, ensuring that the model respects fundamental rights (e.g., IP, reputational rights) is crucial; therefore, this metric is highly relevant for assessing the model’s safety by confirming proper content refusal behavior.
9313	Legal Reasoning Assistant	AIR-Bench-ViolatingSpecificTypesOfRights	This metric is highly relevant as it tests whether the model appropriately refuses to produce content that infringes on recognized legal and fundamental rights, a critical aspect of operating safely in the legal domain.
8330	Financial Portfolio Management	AIR-Bench-UnfairMarketPractices	The metric directly evaluates the system's ability to avoid facilitating harmful or illegal economic practices, making it critically relevant to ensuring ethical and safe operations in financial advisory contexts.
10270	Property Valuation	AIR-Bench-UnfairMarketPractices	By measuring the model’s refusal to engage in unfair market practices that could lead to economic harm, this metric is highly relevant for ensuring the system avoids actions that could contribute to unethical or anticompetitive behavior in the real estate domain.
8547	Tax Compliance Advisor	AIR-Bench-UnfairMarketPractices	Ensuring that the model refuses to assist with practices that could lead to economic harm is highly relevant to a tax compliance advisor, as it helps safeguard against providing advice that might encourage unethical or illegal behaviors in tax planning. This metric directly contributes to mitigating risks related to unintended economic harm, making it a crucial safety measure in this context.
7338	Programmatic Advertising Optimization	AIR-Bench-UnfairMarketPractices	This metric is extremely relevant for safety because it directly measures the model's capacity to avoid facilitating unfair or anticompetitive market practices, a critical concern in preventing economic harm in the advertising domain.
9324	Legal Reasoning Assistant	AIR-Bench-UnfairMarketPractices	The metric is highly relevant from a safety standpoint because it evaluates whether the model can consistently avoid facilitating harmful or unethical strategies, a critical safeguard for a legal assistant that might be presented with queries leaning toward illegal or unethical advice.
10681	Code Generation Assistant	AIR-Bench-UnfairMarketPractices	The metric is highly relevant from a safety perspective as it assesses whether the model can appropriately refuse requests that facilitate unfair market practices, aligning well with the need to prevent harmful or unethical outputs in a software development context.
8492	Insurance Policy Pricing Optimization	AIR-Bench-UnfairMarketPractices	Since the metric evaluates the model’s ability to decline requests that could lead to economic harm or market manipulation, it is highly relevant for ensuring the system operates within ethical and legal bounds in a financial services context.
10926	Personalized Product Recommendations	AIR-Bench-UnfairMarketPractices	The metric is highly relevant for ensuring the system does not contribute to market manipulation or anticompetitive practices, which is vital for maintaining ethical and legally compliant operations in the business context.
10844	Content Moderation	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant for safety because it evaluates the model’s adherence to privacy principles and its ability to avoid producing or disclosing sensitive data—critical safeguards in any content moderation system to protect user privacy and prevent harm.
8198	Personalized Tutor	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric directly addresses critical privacy concerns—ensuring the model does not generate or expose sensitive student or personal data—which is essential for a tutoring system handling educational records and personal information. This makes it extremely relevant for ensuring that the model operates safely and ethically in its educational context.
9237	Contract Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given that contract analysis involves handling confidential and potentially sensitive data, ensuring that the model refuses to process or generate unauthorized sensitive information is critical to avoiding legal and ethical breaches, making this metric extremely relevant for safety.
10956	Personalized Product Recommendations	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric directly addresses privacy and sensitive data handling—critical safety concerns in a personalized recommendation system where customer data is used—making it extremely relevant for ensuring that the model avoids unauthorized privacy violations and data misuse.
11058	Public Transportation Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since public transportation systems may process sensitive data (like location information and personal details of passengers) and must adhere to privacy regulations, this metric is highly relevant for ensuring the system does not facilitate privacy violations or misuse sensitive information.
10261	Property Valuation	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring that the AI system resists unauthorized disclosure or misuse of sensitive data is highly relevant, as property valuation systems may encounter or process private information about property owners and investors, making robust privacy safeguards essential.
9900	Content Recommendation Engine	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is extremely relevant for safety because it rigorously tests the model’s ability to avoid unauthorized privacy violations and handle sensitive data ethically, which is critical in ensuring compliance with privacy standards in media and entertainment.
8498	Insurance Policy Pricing Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric directly evaluates the model’s safeguards against unauthorized privacy violations and mishandling sensitive customer data, making it critically important for maintaining ethical and legal standards in the insurance domain.
7331	Programmatic Advertising Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring that the model refuses unauthorized privacy-related requests is highly relevant to maintaining ethical standards and avoiding legal risks in programmatic advertising, though it is not a complete safety assessment on its own.
8727	Population Health Management	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	This metric is extremely relevant from a safety perspective as it directly addresses the risks of unauthorized access, processing, and exposure of sensitive personal and health data, which are of utmost concern in healthcare applications.
8414	Insurance Claims Fraud Detection	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Because the insurance claims process involves highly sensitive personal and financial data, a metric that tests for unauthorized privacy violations and handling of sensitive data is critically relevant to ensuring safe and ethical operation.
11169	Energy Grid Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring that the system does not inadvertently process or disclose sensitive consumer data is critical in the utility sector. As privacy protection is integral to ethical and safe handling of data, the metric is highly relevant for assessing the model’s safety performance in this context.
8614	Healthcare Resource Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	In the context of healthcare, where patient data privacy and legal compliance are paramount, a metric that rigorously tests for unauthorized privacy violations and misuse of sensitive data is extremely relevant. It directly addresses critical safety concerns by ensuring that the model adheres to privacy principles and relevant ethical and legal standards.
8368	Fraud Detection in Financial Transactions	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring that the model does not enable unauthorized handling of sensitive data or privacy breaches is critical in financial applications, making this metric highly relevant for maintaining secure and compliant operations.
9320	Legal Reasoning Assistant	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is extremely relevant for safety because it directly tests the model’s ability to respect privacy boundaries and avoid unauthorized disclosures, which is critical when handling sensitive legal information and ensuring ethical compliance.
8844	Employee Engagement Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since handling employee data involves significant privacy concerns, a metric that tests and ensures robust refusal of unauthorized privacy violations is critical for safely operating in a human resources context.
9085	Expertise Location System	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since the system deals with employee data—including potentially sensitive information such as location and personal details—the metric is highly relevant for ensuring the model minimizes risks of unauthorized privacy violations and data misuse.
9348	Legal Research Assistant	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	This metric is highly relevant from a safety perspective because respecting privacy and avoiding unauthorized disclosures are crucial in the legal sector, ensuring that the assistant adheres to strict ethical and legal guidelines.
9246	Legal Document Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is extremely relevant from a safety perspective because legal document analysis must strictly adhere to privacy laws and data protection standards, making the test for unauthorized privacy violations and sensitive data handling critically important.
8325	Financial Portfolio Management	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	This metric is highly relevant for safety as it directly evaluates the model’s adherence to privacy principles and its ability to prevent unauthorized processing or disclosure of sensitive financial and personal data, which is critical in the financial services domain.
10083	Drug Safety Monitoring	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given that the use case involves handling highly sensitive health and patient data, the metric is extremely relevant for ensuring data privacy and preventing unauthorized access, which is essential for patient safety and regulatory compliance in the pharmaceutical industry.
10043	Drug Discovery Acceleration	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	In a pharmaceutical context, especially when handling sensitive health or proprietary data, ensuring the model does not facilitate privacy violations is crucial for ethical and legal compliance, making this metric highly relevant for safety.
9174	Knowledge Discovery and Mining	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given the sensitive nature of organizational communications and documents used for knowledge discovery, the ability to prevent unauthorized privacy violations is critical, making this metric extremely relevant to the use case’s safety dimension.
9383	Litigation Prediction	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	In the legal domain, ensuring that the system does not inadvertently leak or misuse sensitive data is crucial; thus, a strong performance on privacy and sensitive data handling is highly relevant for ensuring the model operates safely and ethically.
11035	Fleet Management	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since fleet management involves handling sensitive information such as driver details and location data, a strong measure of unauthorized privacy violations and sensitive data handling is crucial for safe and ethical system operation.
9938	Real-time Content Moderation	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant to safety as it directly evaluates the model’s ability to prevent privacy breaches and handle sensitive data responsibly, both of which are critical ethical and legal safeguards in content moderation.
9141	Internal Knowledge Base Search	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given that the internal knowledge base may contain sensitive or proprietary information, ensuring the model refuses unauthorized privacy-infringing actions is critical, making this metric extremely relevant for safeguarding against privacy violations.
7298	Marketing Attribution Modeling	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant for safety as it directly measures the model’s ability to refrain from unauthorized processing of sensitive personal data—a critical requirement given that marketing attribution systems often handle data involving privacy-sensitive customer information.
7672	Intelligent Customer Support Automation	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric directly assesses the model’s capability to avoid unauthorized data processing and sensitive data exposure, which is critical for customer support environments where privacy protection is paramount.
8571	Diagnostic Support System	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	In healthcare, safeguarding sensitive patient information is paramount; this metric is extremely relevant as it ensures that the AI system adheres to stringent privacy standards and avoids harmful data misuse, directly addressing critical safety concerns.
8649	Medical Image Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	In the healthcare context where models handle sensitive patient data, a metric that ensures strong privacy safeguards is extremely relevant for preventing unauthorized data disclosure and upholding ethical standards.
8079	Design Trend Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since the metric gauges the model’s adherence to privacy principles and its refusal to engage in unauthorized handling of sensitive data, it is highly relevant for ensuring that the system operates ethically and without harm, even in contexts like trend analysis where privacy may still be a concern.
10788	Database Query Optimizer	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Because the use case involves handling potentially sensitive data and ensuring data integrity and security, a metric that rigorously tests the model’s ability to avoid unauthorized privacy violations is critically important to the safe operation of the system.
10891	Cybersecurity Threat Detection	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant for assessing safety since it measures the model’s adherence to privacy principles and refusal of unauthorized data processing—an important aspect when sensitive personal or security-related data is involved.
8869	Employee Performance Analytics	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given that the model will handle sensitive employee-related data, ensuring strict privacy safeguards is critical, making this metric extremely relevant for testing the model’s ability to prevent unauthorized data exposure and privacy breaches.
7840	Military Logistics Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given the sensitive nature of defense data and the critical need to avoid unauthorized privacy violations in military operations, a metric that rigorously tests the model’s commitment to data privacy and resistance to misuse is extremely relevant.
7637	Customer Feedback Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since customer feedback may contain sensitive or personal data, ensuring that the model adheres to privacy standards and refuses unauthorized data handling is highly relevant for safeguarding users and maintaining ethical standards.
10394	Genomic Research Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is extremely relevant for safety because it directly assesses the model’s handling of privacy—critical when working with sensitive genomic and health-related data—to ensure compliance with ethical and legal standards.
7881	Military Training Simulation	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring that the model properly refuses unauthorized handling of sensitive data is crucial in a defense context, where the handling of classified or identifiable information is a significant concern, making this metric highly relevant to the safety and ethical operation of the simulation.
8150	Curriculum Design	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring the model reliably refuses to process or disclose sensitive data is highly relevant in an educational context where student and educational records are involved. This metric directly safeguards against privacy violations and legal risks, making it a crucial safety indicator for the use case.
8721	Patient Risk Prediction	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given the sensitive nature of health data and the critical need to avoid unauthorized disclosures or processing in patient risk prediction, ensuring robust privacy refusals is essential. This metric directly assesses the model’s adherence to data privacy and ethical guidelines, making it extremely relevant for safety.
8277	Credit Risk Predictor	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since credit risk predictors work with sensitive financial and personal data, ensuring that the model does not facilitate unauthorized privacy violations is critical, making this metric extremely relevant to the safety dimension.
7225	Customer Segmentation and Targeting	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant for assessing whether the system appropriately refuses requests that could lead to privacy violations or mishandling of sensitive customer data, which is critical in maintaining ethical data practices in customer segmentation and targeting.
8425	Insurance Claims Processing	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is extremely relevant to safety, as it directly measures the model’s effectiveness in refusing unauthorized processing of sensitive data—a critical requirement for protecting client privacy and complying with legal and ethical standards in the insurance domain.
8218	Student Performance Prediction	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	This metric is highly relevant as it rigorously tests the model’s ability to prevent unauthorized handling of sensitive data, which is critical when dealing with personal and educational records in the student performance context.
10176	Building Performance Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Although building performance systems may not primarily handle sensitive personal data, they can involve occupant or facility information, so ensuring strong safeguards against unauthorized privacy violations is highly relevant for safe and ethical operation.
10687	Code Generation Assistant	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant from a safety perspective because it evaluates whether the model reliably refuses requests that could lead to unauthorized processing or exposure of sensitive data—a critical factor for any AI system, including those generating code.
7700	Virtual Customer Service Agent	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is extremely relevant for ensuring that the virtual agent adheres to privacy and ethical guidelines, which is critical in avoiding unauthorized handling of sensitive customer data.
8120	Automated Assessment & Grading	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Because the automated grading system deals with sensitive educational records and personal data, a robust ability to refuse unauthorized processing or disclosure is critical, making this metric extremely relevant for ensuring ethical and safe operation.
8981	Workflow Attrition Estimator	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring that the system correctly handles sensitive employee data and does not inadvertently violate privacy guidelines is critical in an HR context, making this metric extremely relevant for assessing the safety and ethical compliance of the use case.
8513	Tax Compliance Advisor	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given the financial context and the importance of protecting sensitive financial and personal data, ensuring the model refuses unauthorized privacy actions is highly relevant to avoiding privacy breaches and upholding regulatory compliance.
7754	Autonomous Defense Systems	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	In military contexts, mishandling sensitive or classified data can have severe consequences, making the model’s ability to refuse unauthorized processing critical for safe operation.
9972	Clinical Trial Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given the sensitive nature of patient and medical data in clinical trials, ensuring the model properly refuses actions that violate privacy is critical, making this metric extremely relevant for the use case's safety dimension.
11121	Traffic Management	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant from a safety perspective since traffic management systems often process sensitive location and personal data, making the prevention of privacy violations crucial. Ensuring the system refuses unauthorized privacy incursions directly contributes to the overall ethical and secure operation of the system.
10523	API Integration Assistant	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant from a safety standpoint as it ensures that the model adheres to strict privacy guidelines and refuses to engage in unauthorized processing of sensitive data, which is crucial to avoid harmful data breaches in any API integration context.
9824	Audience Analytics and Insights	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since the use case involves handling potentially sensitive audience data, a strong refusal to facilitate privacy violations is critical, making this metric extremely relevant to assessing the model’s ethical and legal adherence to privacy standards.
10744	Code Refactoring Assistant	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring the model refuses to produce or mishandle sensitive data is important even in a refactoring context, making this privacy-focused metric highly relevant to maintaining ethical and safe operation.
9043	Automated Knowledge Base Maintenance	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since the metric rigorously evaluates the model’s handling of sensitive and personal data to prevent unauthorized disclosures, it is highly relevant for ensuring that the knowledge base maintenance system operates safely and respects privacy guidelines.
9498	Route Optimization	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since the route optimization system handles location and other personal data, a metric that tests for unauthorized privacy violations is critically relevant to ensure the system operates safely, respects privacy, and complies with ethical and legal standards.
8792	AI-Powered Recruitment	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	This metric is extremely relevant for ensuring the model adheres to legal and ethical privacy guidelines, which is critical in a recruitment system that handles sensitive personal information and aims to reduce bias and legal risks.
9007	Workforce Planning and Analytics	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	In an HR context where sensitive employee data may be processed, it is crucial that the model strictly adheres to privacy guidelines, making this metric extremely relevant for ensuring ethical use and preventing privacy violations.
11302	Usage Pattern Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Given that consumer usage data often includes sensitive personal information, ensuring the model refuses unauthorized data processing is critically important; this metric directly assesses such privacy safeguards, making it extremely relevant for the use case’s safety dimension.
10298	Smart Building Management	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	The metric is highly relevant from a safety perspective since managing a smart building involves processing sensitive occupancy and user data, and strong privacy safeguards are critical to protecting individuals and upholding ethical standards.
11228	Smart Grid Management	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since the metric ensures the model robustly avoids handling sensitive data and unauthorized privacy violations, it is extremely relevant for protecting consumer and operational data—a critical safety requirement in utilities.
7593	Automated Email Triage and Response	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Since the system processes potentially sensitive customer and employee communication, a robust safeguard against unauthorized handling of private data is critical; thus, a metric that assesses privacy protection behaviors is extremely relevant for ensuring safety.
7365	Social Media Campaign Analysis	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	Ensuring that a model does not facilitate the misuse or unauthorized processing of sensitive data is highly relevant in a social media context, as it helps prevent privacy breaches and maintains ethical standards, making this metric highly pertinent for evaluating safety.
7598	Automated Email Triage and Response	AIR-Bench-TypesOfDefamation	Ensuring that the system avoids generating defamatory or harmful content is a critical safety concern in customer communications, making this metric highly relevant for evaluating the system's safe operation.
9289	Legal Reasoning Assistant	AIR-Bench-TypesOfDefamation	Since ensuring that the model refrains from producing harmful or false statements is critical—especially in sensitive legal contexts—the metric is highly relevant for assessing the model’s safety and adherence to ethical guidelines in legal applications.
7255	Customer Segmentation and Targeting	AIR-Bench-TypesOfDefamation	Since personalized marketing content must avoid harmful or defamatory language, a measure of defamation refusal is highly relevant for ensuring that the system operates ethically and minimizes risks in communication.
9916	Content Recommendation Engine	AIR-Bench-TypesOfDefamation	Ensuring that the system avoids propagating harmful or defamatory content is critical in media and entertainment, making the defamation refusal measure highly relevant for safeguarding against reputational and ethical risks.
7697	Virtual Customer Service Agent	AIR-Bench-TypesOfDefamation	Since ensuring that the agent does not produce harmful or defamatory content is an important ethical and harm-reduction requirement in customer service, this metric is highly relevant in assessing the safety dimension of the model's operation.
9054	Automated Knowledge Base Maintenance	AIR-Bench-TypesOfDefamation	Since the metric assesses the model’s avoidance of harmful, defamatory outputs—a key safety concern—it is highly relevant for ensuring that the system does not produce damaging or biased content in a knowledge base setting.
8853	Employee Engagement Analysis	AIR-Bench-TypesOfDefamation	Ensuring that the model avoids producing defamatory content is quite important in HR applications to prevent legal risks and ethical issues, making this metric highly relevant for the safety dimension of the use case.
9357	Legal Research Assistant	AIR-Bench-TypesOfDefamation	In the legal domain, ensuring that the model does not produce defamatory, biased, or harmful content is critical; therefore, a metric that tests refusal capabilities for defamation is highly relevant to prevent reputational or legal harm.
10859	Content Moderation	AIR-Bench-TypesOfDefamation	This metric directly assesses the model’s capacity to avoid generating defamatory (and thus potentially harmful) content, which is critical for preventing real-world harm and ensuring the system adheres to ethical guidelines in content moderation.
9962	Real-time Content Moderation	AIR-Bench-TypesOfDefamation	Since preventing defamatory or harmful statements is central to safe content moderation, this metric is highly relevant for evaluating whether the model adheres to ethical guidelines and minimizes potential harm, despite being focused on a particular risk category.
7657	Intelligent Customer Support Automation	AIR-Bench-TypesOfDefamation	The metric is highly relevant from a safety perspective as it measures the ability to avoid harmful outputs like defamation, aligning well with ethical guidelines and ensuring that the automated support does not produce damaging statements.
10857	Content Moderation	AIR-Bench-SupportingMaliciousOrganizedGroups	Since the metric gauges the refusal of assistance for violent extremist and criminal group support, it is critical in ensuring the system does not inadvertently promote harmful behavior, making it extremely relevant from a safety perspective.
9910	Content Recommendation Engine	AIR-Bench-SupportingMaliciousOrganizedGroups	Ensuring that the AI avoids providing or endorsing extremist or harmful content is critical in a media context, making this metric extremely relevant for safeguarding users and the platform.
7887	Military Training Simulation	AIR-Bench-SupportingMaliciousOrganizedGroups	Given the high risk associated with misuse of AI in defense contexts, ensuring the model does not inadvertently support extremist, terrorist, or criminal actions is critical. This metric directly addresses content safety, aligning extremely well with ethical and harm prevention requirements.
9961	Real-time Content Moderation	AIR-Bench-SupportingMaliciousOrganizedGroups	The metric is extremely relevant for safety as it directly measures the model’s ability to avoid facilitating violent or extremist content, which is critical in preventing harm in content moderation settings.
7802	Military Intelligence Analysis	AIR-Bench-SupportingMaliciousOrganizedGroups	Given the defense context, it is critical that the system robustly avoids generating support for violent, extremist, or criminal activities. This metric directly tests an important safety mechanism, ensuring the AI’s outputs remain ethical and do not inadvertently aid dangerous groups.
7669	Intelligent Customer Support Automation	AIR-Bench-SupportingMaliciousOrganizedGroups	Ensuring that the model does not inadvertently support extremist or criminal organizations is highly relevant for ethical and safe operations, even in a customer support context where occasional sensitive queries may arise.
9300	Legal Reasoning Assistant	AIR-Bench-SupportingMaliciousOrganizedGroups	Ensuring that the model reliably refuses support for violent and criminal groups is highly relevant for maintaining ethical standards and preventing harm, especially in legal contexts where misuse could have serious consequences.
9951	Real-time Content Moderation	AIR-Bench-SuicidalAndNonSuicidalSelfInjury	Given the high stakes around self-harm content, a metric that assesses the system’s refusal to provide guidance or promote harmful behaviors is extremely important, ensuring that the model mitigates risks and adheres to critical ethical standards in a real-time moderation context.
8592	Diagnostic Support System	AIR-Bench-SuicidalAndNonSuicidalSelfInjury	Ensuring that the system appropriately handles self-harm related content is critical in healthcare, especially given the vulnerable nature of patients; thus, while not addressing every safety concern, the metric is highly relevant for evaluating safe interactions in potentially sensitive mental health scenarios.
7729	Virtual Customer Service Agent	AIR-Bench-SuicidalAndNonSuicidalSelfInjury	The metric is highly relevant from a safety perspective because it evaluates the model’s capacity to correctly refuse to provide harmful self-harm guidance—an important safeguard if a customer in distress interacts with the agent.
8185	Personalized Tutor	AIR-Bench-SuicidalAndNonSuicidalSelfInjury	In the educational context—where vulnerable students might inquire about self-harm—a strong ability to safely manage such requests significantly contributes to preventing harm and adhering to ethical guidelines, making this metric highly relevant.
10866	Content Moderation	AIR-Bench-SuicidalAndNonSuicidalSelfInjury	The metric directly assesses the model’s ability to refuse or handle self-harm content safely, which is vital for preventing harm and ensuring ethical moderation; therefore, it provides critical safety insights for this use case.
7260	Customer Segmentation and Targeting	AIR-Bench-SowingDivision	Ensuring that the system does not generate or support divisive content is important in any consumer-facing application, making this metric highly relevant for preventing unethical or harmful messaging even if it is not the primary function of the use case.
9936	Real-time Content Moderation	AIR-Bench-SowingDivision	Since the metric specifically evaluates the model’s tendency to avoid generating or propagating content that could incite internal conflict and social division, it is critically relevant for ensuring that the moderation system operates ethically and safely in live environments.
8206	Personalized Tutor	AIR-Bench-SowingDivision	Ensuring that a tutoring system does not generate harmful or divisive content is critical in education, where influencing young minds ethically is paramount. Thus, the metric is highly relevant for evaluating the safety aspect of content generation in this context.
8828	Employee Engagement Analysis	AIR-Bench-SowingDivision	Ensuring the system avoids generating divisive or manipulative content is highly relevant for maintaining ethical workplace practices and avoiding harm, making this metric strongly applicable to the safety dimension.
9891	Content Recommendation Engine	AIR-Bench-SowingDivision	Since the metric directly tests the model's ability to avoid sowing social division—a critical safety concern in content recommendation—it is extremely relevant to ensuring that harmful or polarizing recommendations are not propagated.
10834	Content Moderation	AIR-Bench-SowingDivision	The metric directly assesses the model’s ability to avoid exacerbating societal conflict by refusing harmful, divisive content, making it critical for ensuring safe operations in content moderation systems.
10865	Content Moderation	AIR-Bench-PoliticalPersuasion	The metric is highly relevant for ensuring political neutrality and avoiding misuse in politically persuasive or biased ways, which is an important safety concern in content moderation. It provides useful information on how well the model adheres to ethical guidelines regarding political content.
9965	Real-time Content Moderation	AIR-Bench-PoliticalPersuasion	Ensuring a model’s political neutrality and its refusal to generate political campaign materials is critical for maintaining ethical boundaries and preventing harmful misuse, making the metric highly relevant from a safety perspective.
7379	Social Media Campaign Analysis	AIR-Bench-PoliticalPersuasion	Ensuring political neutrality is critical in avoiding the misuse of the tool for partisan persuasion. Although the metric targets political campaigning specifically, its role in preventing politically biased outputs is highly relevant to maintaining ethical and safe operations in marketing environments.
8845	Employee Engagement Analysis	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring the model does not perpetuate harmful stereotypes or biased views is highly relevant in HR contexts, where outputs must be ethically sound and non-discriminatory to protect employee well‐being.
7984	Automated Design Generation	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring that the AI does not reinforce harmful stereotypes or discriminatory views is important for ethical operation and client trust, making this metric highly relevant to the safety aspect of the use case.
8800	AI-Powered Recruitment	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring the system does not perpetuate harmful stereotypes or discriminatory ideologies is critical in HR applications. The metric is highly relevant for the safety dimension as it directly addresses the need to avoid bias and uphold ethical hiring practices.
8135	Curriculum Design	AIR-Bench-PerpetuatingHarmfulBeliefs	Since educational content must be free from harmful biases and stereotypes, a metric that tests the model's ability to avoid perpetuating harmful beliefs is highly relevant to ensuring safe and ethical curriculum development.
8937	Resume Screener	AIR-Bench-PerpetuatingHarmfulBeliefs	Since a resume screener must mitigate discriminatory practices and avoid reinforcing harmful biases, a metric focused on harmful belief perpetuation is highly relevant to ensuring the system operates ethically and safely.
9018	Workforce Planning and Analytics	AIR-Bench-PerpetuatingHarmfulBeliefs	This metric is highly relevant for ensuring that the model does not inadvertently reinforce harmful stereotypes or biases, which is critical in HR applications where fair and non-discriminatory decision-making is essential for ethical resource allocation and workforce planning.
9412	Litigation Prediction	AIR-Bench-PerpetuatingHarmfulBeliefs	Since legal applications must avoid biased or discriminatory reasoning, this metric is highly relevant in ensuring the model does not produce outputs that reinforce harmful stereotypes, which is critical for ethical and unbiased legal decision support.
9945	Real-time Content Moderation	AIR-Bench-PerpetuatingHarmfulBeliefs	This metric directly addresses the prevention of perpetuating harmful beliefs by measuring how the system refuses to propagate discriminatory content, making it crucial for ensuring ethical compliance and user safety in the content moderation use case.
9104	Expertise Location System	AIR-Bench-PerpetuatingHarmfulBeliefs	Since ensuring fair, unbiased employee matching is critical and harmful generalizations or stereotypes can cause significant HR and ethical issues, a metric that tests the model’s refusal to perpetuate hateful or discriminatory content is highly relevant to the system’s safe operation.
7655	Intelligent Customer Support Automation	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring that the chatbot does not perpetuate harmful beliefs is crucial for maintaining ethical and unbiased customer interactions, making this metric highly relevant from a safety perspective.
7330	Programmatic Advertising Optimization	AIR-Bench-PerpetuatingHarmfulBeliefs	Evaluating the refusal of hate or toxic content is critical for ensuring that advertising systems avoid perpetuating harmful stereotypes or biased content, making it extremely relevant from a safety perspective.
7702	Virtual Customer Service Agent	AIR-Bench-PerpetuatingHarmfulBeliefs	Since safety in customer interactions is paramount—ensuring that the agent does not perpetuate hate, toxicity, or biased content—the metric is extremely relevant for gauging and safeguarding against harmful outputs in a customer service context.
7571	Automated Email Triage and Response	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring the system does not perpetuate harmful stereotypes or discriminatory language is crucial for ethical customer interactions, making this metric highly relevant to the safety aspect of the use case.
10849	Content Moderation	AIR-Bench-PerpetuatingHarmfulBeliefs	The metric is specifically designed to assess the avoidance of perpetuating harmful stereotypes and discriminatory ideologies, making it critically relevant for ensuring that content moderation does not inadvertently contribute to bias or harm.
9057	Automated Knowledge Base Maintenance	AIR-Bench-PerpetuatingHarmfulBeliefs	The metric is highly relevant for safety since preventing the reinforcement of harmful stereotypes is critical in ensuring that the knowledge base remains free from biased or discriminatory content, aligning well with ethical and harm-avoidance standards.
8119	Automated Assessment & Grading	AIR-Bench-PerpetuatingHarmfulBeliefs	In an educational setting, it is critical that automated grading and feedback do not reinforce harmful stereotypes or discriminatory ideologies. This metric directly assesses the system’s ability to guard against content that could harm students or perpetuate bias, making its relevance to safety extremely high.
8175	Personalized Tutor	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring that an AI-powered tutor does not perpetuate harmful stereotypes or discriminatory ideologies is critical in an educational setting, making this metric extremely relevant for evaluating the system’s safe operation and ethical compliance.
7791	Military Intelligence Analysis	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring that the model avoids perpetuating harmful stereotypes or biased narratives is highly relevant in the defense context, as misrepresentation or biased outputs could lead to severe ethical and strategic consequences.
7642	Customer Feedback Analysis	AIR-Bench-PerpetuatingHarmfulBeliefs	The metric is highly relevant from a safety perspective because it directly evaluates the model’s safeguards against perpetuating harmful stereotypes and discriminatory language, an important consideration when handling sensitive customer data and ensuring ethical outcomes.
9820	Audience Analytics and Insights	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring that the system does not perpetuate harmful stereotypes is crucial in the media context, where biased analyses could lead to unethical targeting or misinterpretation. Therefore, this safety metric is highly relevant, as it measures a critical aspect of ethical and safe model behavior in content-driven industries.
7248	Customer Segmentation and Targeting	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring that the model does not perpetuate harmful stereotypes or biases is critical in a marketing context, making this metric extremely relevant to maintaining ethical standards and preventing reputational or customer harm.
9311	Legal Reasoning Assistant	AIR-Bench-PerpetuatingHarmfulBeliefs	Ensuring the model does not perpetuate harmful stereotypes is particularly important in legal contexts where biased outputs can have significant real-world consequences, making this metric highly relevant for safety.
9902	Content Recommendation Engine	AIR-Bench-PerpetuatingHarmfulBeliefs	This metric is extremely relevant for safety because it directly measures the system’s capability to avoid delivering or reinforcing harmful, biased, or discriminatory content, a critical concern for content recommendation in media and entertainment.
7770	Autonomous Defense Systems	AIR-Bench-OtherUnlawfulCriminalActivities	Since the metric evaluates the model’s ability to decline participation in activities that could undermine national security and other critical legal and ethical boundaries, it is highly relevant to ensuring that the system operates safely and ethically in a military context.
9318	Legal Reasoning Assistant	AIR-Bench-OtherUnlawfulCriminalActivities	Since the legal assistant must avoid facilitating or abetting unlawful behavior, a metric testing for refusal of criminal activities is highly relevant to ensuring safe and ethical operation in legal contexts.
10684	Code Generation Assistant	AIR-Bench-OtherUnlawfulCriminalActivities	Ensuring the model refuses to facilitate unlawful or harmful activities is a key safety requirement for a code generation assistant, making this metric highly relevant to protecting against misuse, even though its focus isn’t uniquely tailored to coding tasks.
10873	Content Moderation	AIR-Bench-OtherUnlawfulCriminalActivities	Ensuring that the system robustly refuses to facilitate unlawful or harmful content is critical for maintaining user safety and upholding ethical guidelines, making this metric extremely relevant for the safety dimension in content moderation.
9341	Legal Research Assistant	AIR-Bench-OffensiveLanguage	Ensuring that the model avoids generating offensive, vulgar, or derogatory language is highly relevant for safety, as it helps prevent harmful outputs and maintains ethical standards in sensitive legal contexts. This metric’s focus on content safety is important for protecting both legal professionals and clients from potentially damaging language.
7384	Social Media Campaign Analysis	AIR-Bench-OffensiveLanguage	Since the system may be used to generate content or recommendations, ensuring that it avoids offensive, derogatory, or hateful language is important for ethical operation and reputational risk mitigation, making this metric highly relevant from a safety perspective.
9030	Automated Knowledge Base Maintenance	AIR-Bench-OffensiveLanguage	The metric is highly relevant from a safety perspective because ensuring that the AI system refrains from using or propagating offensive or derogatory language directly supports maintaining a respectful and ethical environment, which is crucial in the handling and dissemination of knowledge base content.
10870	Content Moderation	AIR-Bench-OffensiveLanguage	This metric is extremely relevant to safety because ensuring that the system neither generates nor propagates offensive or hateful language is critical for preventing harm, reducing toxic content, and adhering to ethical guidelines.
7229	Customer Segmentation and Targeting	AIR-Bench-OffensiveLanguage	Avoiding hate speech and derogatory language is crucial in any customer-facing application, ensuring that communications remain ethical and non-harmful, particularly when personalizing marketing messages.
8782	AI-Powered Recruitment	AIR-Bench-OffensiveLanguage	Ensuring that the model refrains from generating offensive, vulgar, or derogatory language is very important in HR contexts to avoid harm and bias, making this metric highly relevant for the safe operation of the system.
9960	Real-time Content Moderation	AIR-Bench-OffensiveLanguage	Since the metric is designed to evaluate how effectively the model avoids generating offensive, vulgar, or derogatory comments, it is highly relevant to ensuring that the system operates without causing harm or propagating bias in a live content moderation setting.
7671	Intelligent Customer Support Automation	AIR-Bench-OffensiveLanguage	This metric is extremely relevant for ensuring that the AI avoids generating hateful, toxic, or derogatory language, which is critical for reducing harm, upholding ethical standards, and maintaining customer trust in a support environment.
9817	Audience Analytics and Insights	AIR-Bench-OffensiveLanguage	Ensuring that the system refrains from generating offensive, vulgar, or derogatory language is highly relevant for maintaining ethical standards and public trust in media and entertainment contexts. This metric directly supports the safe operation of the AI by preventing harmful outputs.
8193	Personalized Tutor	AIR-Bench-OffensiveLanguage	Ensuring that the tutor avoids generating hate or toxic language is critical in educational contexts, especially when dealing with diverse student populations, so this metric is highly relevant for maintaining a safe and respectful learning environment.
9897	Content Recommendation Engine	AIR-Bench-OffensiveLanguage	Ensuring that the system avoids hate, toxicity, and other offensive language is crucial for protecting users and upholding ethical standards, making this metric highly relevant from a safety perspective for a content recommendation engine.
7985	Automated Design Generation	AIR-Bench-OffensiveLanguage	Ensuring the model avoids generating offensive, vulgar, or hateful language is important for maintaining ethical standards and protecting client reputation, especially in a customer-facing service, even though it does not cover all safety aspects.
8099	Automated Assessment & Grading	AIR-Bench-OffensiveLanguage	Ensuring that the model refrains from using hate or toxic language is highly relevant in education, as maintaining a respectful tone in automated feedback is crucial for ethical and safe interactions with students and other stakeholders.
7719	Virtual Customer Service Agent	AIR-Bench-OffensiveLanguage	The metric is extremely relevant for safety because it directly assesses the model’s ability to avoid generating offensive, derogatory, or toxic language, ensuring that the agent adheres to ethical and content safety guidelines crucial for customer interactions.
8847	Employee Engagement Analysis	AIR-Bench-OffensiveLanguage	Ensuring that the model avoids generating hate speech or vulgar content is highly relevant in a sensitive HR context, as offensive language could undermine trust and cause harm, making this metric crucial for assessing the model's adherence to ethical and safety guidelines.
7603	Automated Email Triage and Response	AIR-Bench-OffensiveLanguage	Avoiding hate, toxicity, or derogatory language is critical in ensuring that automated email responses do not cause harm or damage the organization’s reputation, making this metric highly relevant for the safety dimension of the use case.
7647	Customer Feedback Analysis	AIR-Bench-OffensiveLanguage	Ensuring that the model refrains from generating hate, vulgar, or derogatory language is very important in a customer service context, where outputs must be ethical and non-harmful to both customers and employees.
9964	Real-time Content Moderation	AIR-Bench-NonConsensualNudity	Since preventing the generation or spread of non-consensual intimate imagery is a critical safety objective in content moderation, this metric is extremely relevant for assessing whether the system can avoid causing harm and adhere to ethical guidelines.
10851	Content Moderation	AIR-Bench-NonConsensualNudity	Since the metric directly tests the system’s ability to avoid engaging with content that could lead to serious ethical violations—such as non-consensual intimate imagery—it is extremely relevant to ensuring that the system operates safely and minimizes harm.
10869	Content Moderation	AIR-Bench-Monetized	Since preventing the promotion of harmful or regulated sexual content is critical for protecting users and ensuring ethical guidelines are met, the metric is highly relevant to safety, even though it targets only a subset of the broader safety challenges in content moderation.
7773	Autonomous Defense Systems	AIR-Bench-Misrepresentation	Ensuring that an AI system does not engage in misrepresentation and deception is critical in high-stakes defense environments, making this metric highly relevant for safeguarding against potential misuse or unsafe communications.
8567	Diagnostic Support System	AIR-Bench-Misrepresentation	In healthcare, ensuring that the model does not misrepresent its outputs or impersonate authoritative sources is critical to avoid patient harm and maintain trust, making this metric highly relevant to assessing safety.
11125	Traffic Management	AIR-Bench-Misrepresentation	In the context of Traffic Management, any misrepresentation (e.g., false traffic data or misleading information) can have significant safety implications, impacting emergency response and public trust; thus, the metric’s focus on preventing deceptive outputs is highly relevant to ensuring safe operation.
7582	Automated Email Triage and Response	AIR-Bench-Misrepresentation	The metric is highly relevant to ensuring that the system avoids deceptive behavior such as impersonation or misleading users, which is critical in automated communications to maintain transparency and ethical standards.
7720	Virtual Customer Service Agent	AIR-Bench-Misrepresentation	Ensuring that the virtual agent does not mislead users or impersonate human agents is critical for maintaining trust and ethical operation, making this metric highly relevant to the safety dimension.
7679	Intelligent Customer Support Automation	AIR-Bench-Misrepresentation	Since customer support chatbots must transparently disclose their nature and avoid misleading users, this metric is highly relevant for ensuring that the system refrains from impersonation and unethical representations, aligning well with safety and ethical guidelines.
9322	Legal Reasoning Assistant	AIR-Bench-Misrepresentation	Since legal applications demand transparency and accuracy, ensuring the system avoids misrepresentation and deceptive outputs is highly relevant to safety in legal contexts, making this metric critical in guarding against harmful or misleading legal guidance.
9366	Legal Research Assistant	AIR-Bench-Misrepresentation	Since the metric measures the model’s ability to refuse manipulative behaviors like impersonation or deceptive misrepresentation, it is highly relevant for ensuring ethical and transparent operations in scenarios where legal integrity and clear accountability are critical.
8333	Financial Portfolio Management	AIR-Bench-Misrepresentation	In financial services, ensuring that recommendations are presented transparently and without misrepresentation is vital. This metric is highly relevant from a safety standpoint because it gauges the model’s refusal to engage in deceptive or manipulative behavior, which is critical to maintaining trust and preventing harm in a financial advisory context.
9818	Audience Analytics and Insights	AIR-Bench-Misrepresentation	Since the use case operates in a domain sensitive to authenticity and transparency, the metric is highly relevant—it helps ensure the model does not facilitate misrepresentation or impersonation that could negatively impact audience trust and integrity.
10858	Content Moderation	AIR-Bench-Misrepresentation	The metric is highly relevant to safety because it evaluates whether the model can prevent misuse through deceptive behaviors such as impersonation or misrepresentation—risks that are critical to manage in environments where user trust and the ethical operation of content moderation are paramount.
7385	Social Media Campaign Analysis	AIR-Bench-Misrepresentation	Because the use case involves managing high-visibility social media campaigns, ensuring that the model refrains from misrepresentation and imposter behaviors is highly relevant to mitigating risks associated with deceptive content and preserving transparency.
8181	Personalized Tutor	AIR-Bench-Misrepresentation	In a personalized tutoring system, preventing misrepresentation and ensuring transparency is crucial, especially because students and teachers depend on accurate, honest interactions. The metric's focus on refusing deceptive behavior is highly relevant to maintaining ethical standards and safeguarding users from manipulation.
9952	Real-time Content Moderation	AIR-Bench-Misrepresentation	Since the metric evaluates refusal to misrepresent or impersonate—which is critical for preventing harmful, deceptive content—it is highly relevant for ensuring safe and ethical operations in a real‐time content moderation system.
7318	Programmatic Advertising Optimization	AIR-Bench-Misrepresentation	Since ethical advertising practices require transparency and avoidance of deceptive representations, a metric that assesses misrepresentation risks is highly relevant to ensuring the model operates safely and adheres to ethical guidelines in advertising contexts.
8745	Population Health Management	AIR-Bench-Misrepresentation	The metric is highly relevant from a safety standpoint as it measures the model’s ability to avoid misrepresentations and deceptive outputs, which is critical in healthcare applications where transparency and trust are essential for patient safety and ethical public health management.
7249	Customer Segmentation and Targeting	AIR-Bench-Misrepresentation	Since the metric evaluates the model’s refusal to engage in deceptive practices like impersonation or unlabeled AI-generated content, it is highly relevant for ensuring that the system operates ethically and without harmful misrepresentation in marketing contexts.
7810	Military Intelligence Analysis	AIR-Bench-Misrepresentation	In the defense context, preventing misrepresentation is critical to avoid deceptive outputs that could mislead decision-makers or be exploited adversarially. This metric is highly relevant for ensuring ethical and transparent operation, a vital safety requirement for military intelligence applications.
9928	Content Recommendation Engine	AIR-Bench-Misrepresentation	Evaluating a model’s refusal to misrepresent or impersonate is highly relevant to ensuring ethical outcomes and preventing harmful misinformation, which is vital in media and entertainment contexts. This metric directly addresses risks of manipulation and transparency—core safety concerns even if they are somewhat tangential to the primary recommendation task.
7766	Autonomous Defense Systems	AIR-Bench-Misinformation	In a military context, the inadvertent generation or spread of misinformation could lead to critical errors and dangerous escalations, making the refusal ability a key safety concern. Thus, even though the metric is not designed specifically for this use case, its relevance to preventing harmful deceptive outputs is highly pertinent for maintaining the system’s ethical and secure operation.
7646	Customer Feedback Analysis	AIR-Bench-Misinformation	The metric is highly relevant for safety since it tests the model’s capacity to avoid spreading misinformation and producing fake online content—critical safeguards in a customer service context where fake reviews or deceptive signals could lead to poor business decisions.
8157	Curriculum Design	AIR-Bench-Misinformation	In an educational context, ensuring that curriculum content is not misleading or deceptive is critical, so a metric that tests for misinformation and refusal of deceptive practices is highly relevant for guarding against harmful content and maintaining ethical standards.
9299	Legal Reasoning Assistant	AIR-Bench-Misinformation	Preventing the spread of misinformation is highly important in the legal domain, where inaccurate or misleading information can have serious real‐world consequences, so this metric is quite relevant for ensuring safe and ethically responsible outputs.
10350	Climate Modeling	AIR-Bench-Misinformation	Given the societal risks associated with the spread of climate misinformation—such as misleading policymakers and the public—this metric is highly relevant for evaluating whether the system avoids generating or promoting deceptive or false content in a critical domain.
8200	Personalized Tutor	AIR-Bench-Misinformation	Ensuring that the tutor does not introduce or propagate misinformation is crucial for protecting students from learning incorrect material and upholding ethical standards, which makes this metric highly relevant for the safety aspect of the use case.
9889	Content Recommendation Engine	AIR-Bench-Misinformation	Since misinformation can cause significant harm, especially in media and entertainment, this metric is extremely relevant—it critically evaluates the system’s safeguard against misinformation and deceptive practices, thereby ensuring ethical operation and user protection.
9939	Real-time Content Moderation	AIR-Bench-Misinformation	The metric is highly relevant for safety as it focuses on the model’s refusal to promote misinformation and deceptive practices, which directly aligns with mitigating societal risks and reducing harmful content in a live, user-driven environment.
10837	Content Moderation	AIR-Bench-Misinformation	Since preventing the spread of misinformation is a key safety concern for platforms, this metric is highly relevant in assessing the model's risk-aware behavior and its ability to mitigate harmful societal risks, even though it focuses mainly on one type of potential harm.
8733	Population Health Management	AIR-Bench-Misinformation	In a healthcare context, particularly in managing population health where misinforming trends can result in harmful public health decisions, a strong ability to prevent the spread of misinformation is critical, making this metric extremely relevant for ensuring safe, ethical operations.
7693	Virtual Customer Service Agent	AIR-Bench-Misinformation	The metric is highly relevant for assessing the agent’s safety by ensuring it refrains from generating or promoting misinformation, which is crucial for maintaining ethical standards and trustworthiness in customer communications.
9826	Audience Analytics and Insights	AIR-Bench-Misinformation	Since the use case is in media and entertainment—where misinformation can lead to harmful content strategies—the metric is highly relevant to ensuring the model does not inadvertently support deceptive practices or astroturfing.
7651	Intelligent Customer Support Automation	AIR-Bench-Misinformation	This metric is highly relevant for safety as it evaluates the model's ability to avoid spreading misinformation, which is critical to maintaining ethical standards and preventing harm within customer support interactions.
7319	Programmatic Advertising Optimization	AIR-Bench-Misinformation	For an advertising system, ensuring that the model does not generate deceptive content is important to avoid harmful misinformation, making this metric quite relevant for assessing safe operation and ethical compliance.
9337	Legal Research Assistant	AIR-Bench-Misinformation	Since legal research outputs can directly impact case preparation and possibly client outcomes, ensuring that the model does not generate or propagate misinformation is highly relevant to safely operating in the legal domain.
7361	Social Media Campaign Analysis	AIR-Bench-Misinformation	Ensuring that the model avoids spreading misinformation is crucial in maintaining ethical standards and trust in marketing communications, making this metric highly relevant for the safe operation of the system.
7788	Military Intelligence Analysis	AIR-Bench-Misinformation	In military intelligence contexts, avoiding the spread of deception or false information is critical to preventing misinformed decisions and severe operational risks, making this metric extremely relevant for ensuring safe deployment.
9059	Automated Knowledge Base Maintenance	AIR-Bench-Misinformation	The metric is highly relevant from a safety perspective, as it directly assesses the model’s ability to identify, refuse, and avoid propagating misinformation or deceptive content, which is critical when maintaining a reliable and ethically sound knowledge base.
7844	Military Logistics Optimization	AIR-Bench-MilitaryAndWarfare	The metric is highly relevant from a safety standpoint since it evaluates the model’s capacity to avoid providing harmful or ethically problematic guidance related to military operations, a crucial safeguard in any defense-related application.
7813	Military Intelligence Analysis	AIR-Bench-MilitaryAndWarfare	Since the use case involves sensitive military information, ensuring that the model reliably refuses requests for tactical military advice is critical for preventing misuse; therefore, the metric is extremely relevant for its safety assessment.
7748	Autonomous Defense Systems	AIR-Bench-MilitaryAndWarfare	The metric is specifically designed to assess how well the model refrains from providing dangerous military or strategic advice, which is critical for ensuring safe operation in high-stakes defense contexts.
7871	Military Training Simulation	AIR-Bench-MilitaryAndWarfare	This metric is extremely relevant from a safety perspective as it directly tests the model’s adherence to content safety guidelines in military contexts, ensuring it avoids facilitating harmful military operations.
9702	Predictive Maintenance	AIR-Bench-Integrity	This metric is highly relevant to safety because ensuring data integrity and preventing unauthorized modifications are critical to avoiding scenarios that could lead to unsafe operations or system malfunctions in a manufacturing environment.
7920	Asset Management and Organization	AIR-Bench-Integrity	The metric is highly relevant to safety since ensuring data integrity against unauthorized modifications, malware, and other security risks is crucial for preserving the trustworthiness and protection of design assets.
8373	Fraud Detection in Financial Transactions	AIR-Bench-Integrity	The metric is extremely relevant from a safety perspective because maintaining data integrity—and refusing actions that risk system compromise—is critical for preventing malicious tampering and ensuring secure financial transactions.
10776	Database Query Optimizer	AIR-Bench-Integrity	This metric is highly relevant to safety since it directly evaluates the model’s ability to prevent actions that could compromise data integrity, aligning closely with the critical safety needs in maintaining secure database operations.
8230	Student Performance Prediction	AIR-Bench-Integrity	Ensuring data integrity and resisting unauthorized modifications is vital when handling sensitive student data. This metric is quite relevant for maintaining safety by protecting against scenarios that could compromise the system’s integrity and privacy safeguards.
9753	Production Process Optimization	AIR-Bench-Integrity	The metric directly evaluates the model’s defenses against security risks—such as unauthorized data modification and control override—which are critical to ensuring that a manufacturing optimization system does not compromise operational safety and integrity.
9138	Internal Knowledge Base Search	AIR-Bench-Integrity	The metric is highly relevant from a safety perspective since it measures the model’s ability to reject requests that could compromise data integrity or system controls—an important safeguard in an internal knowledge base environment.
11043	Fleet Management	AIR-Bench-Integrity	Because a breach in data integrity or unauthorized system manipulation could jeopardize vehicle safety, driver wellbeing, and overall system reliability in fleet management, this metric is critical in assessing and ensuring the system’s safe operation.
7841	Military Logistics Optimization	AIR-Bench-Integrity	In a defense context, protecting data integrity and system controls against malicious actions like tampering or control overrides is critical; hence, the metric is extremely relevant for ensuring the system operates safely without causing harm.
9607	Assembly Line Optimization	AIR-Bench-Integrity	The metric directly tests for resilience against security threats (e.g., data tampering and control override), which is highly pertinent to ensuring safe and secure operation in a manufacturing environment where control systems and data integrity are critical.
7557	Precision Farming	AIR-Bench-Integrity	Ensuring data integrity and refusing harmful system modifications are vital to prevent malicious interference—essential for a precision farming system where erroneous control commands could lead to significant resource wastage or crop damage. While not tailored exclusively for agriculture, it is highly relevant to maintaining safe, reliable operations in such settings.
10679	Code Generation Assistant	AIR-Bench-Integrity	For a Code Generation Assistant, safely refusing requests that could compromise data integrity or introduce security vulnerabilities is critical; this metric directly measures the model’s ability to avoid generating code that could be exploited, making it highly relevant for safety.
10133	Manufacturing Quality Control	AIR-Bench-Integrity	The metric is extremely relevant for safety, as it measures the model’s resistance to security threats like data tampering and unauthorized control changes, which are critical for ensuring compliance, quality, and the overall safe operation of pharmaceutical manufacturing processes.
9173	Knowledge Discovery and Mining	AIR-Bench-Integrity	Ensuring that the model can proactively refuse requests that may lead to data tampering or misuse is highly relevant for safe operation, especially when handling sensitive organizational documents and communications.
7498	Livestock Health Monitoring	AIR-Bench-Integrity	Ensuring data integrity and preventing unauthorized modifications is critical in a livestock monitoring system, as compromised data could lead to harmful misinterpretations affecting animal welfare and disease management. The metric is thus highly relevant for safeguarding against safety risks.
10902	Cybersecurity Threat Detection	AIR-Bench-Integrity	By measuring the ability to detect and refuse scenarios that could lead to integrity breaches or malicious control manipulation, the metric is critically aligned with ensuring that the system avoids harm and complies with cybersecurity safety standards.
9525	Supply Chain Optimization	AIR-Bench-Integrity	Since supply chain systems depend heavily on the integrity of data to drive operational decisions, ensuring the model can refuse requests that may lead to tampering or security breaches is highly relevant for maintaining system safety and data accuracy.
10400	Genomic Research Analysis	AIR-Bench-Integrity	The metric is highly relevant for safety as it tests the model’s ability to detect and refuse requests that could compromise data integrity, which is critical for ensuring that genomic research data remains accurate and free from malicious interference.
8581	Diagnostic Support System	AIR-Bench-Integrity	Ensuring that the system can robustly refuse requests that could compromise data integrity is crucial for preventing harmful data tampering and maintaining trust in patient outcomes, making this metric highly relevant for the safety dimension.
7432	Agricultural Yield Optimization	AIR-Bench-Integrity	Ensuring data integrity and preventing unauthorized tampering is vital, as any malicious modification could lead to dangerous mismanagement of farm resources and incorrect decisions. This metric is highly relevant in assessing the system’s ability to safeguard against security risks that might otherwise cause significant harm in an agricultural context.
9572	Warehouse Automation	AIR-Bench-Integrity	The metric is highly relevant here since it directly evaluates the system’s ability to refuse operations that could lead to unauthorized data modifications or control overrides, which is critical for preventing safety hazards in robotic and automated warehouse environments.
9015	Workforce Planning and Analytics	AIR-Bench-Integrity	Ensuring that the model can correctly refuse requests that might lead to data tampering, malware, or unauthorized modifications is crucial for safeguarding integrity in HR applications, making this metric highly relevant to the safety dimension of the use case.
8442	Insurance Claims Processing	AIR-Bench-Integrity	This metric is highly relevant from a safety perspective because defending against unauthorized data modifications, tampering, or control overrides is crucial in protecting the integrity of sensitive financial data and maintaining robust security in an insurance claims processing system.
8483	Insurance Policy Pricing Optimization	AIR-Bench-Integrity	Ensuring the model resists scenarios that could compromise data integrity is highly relevant to financial applications like insurance pricing, where unauthorized modifications could lead to severe economic and reputational harm. The metric directly addresses the security and safety concerns critical in this domain.
11056	Public Transportation Optimization	AIR-Bench-Integrity	The metric is highly relevant for safety as it evaluates the model’s ability to refuse actions that risk system security and data tampering, which is critical for preventing disruptions and ensuring reliable service in a transportation context.
10843	Content Moderation	AIR-Bench-Integrity	The metric is highly relevant from a safety standpoint as it assesses the model's capacity to detect and refuse requests that could compromise data integrity or system controls—an important aspect of mitigating harmful, malicious behavior in a content moderation context.
10221	Construction Project Planning	AIR-Bench-Integrity	Ensuring that the AI system refuses requests that lead to unauthorized data modification is highly relevant in this use case, as breaches in data integrity can severely undermine project planning, risk management, and overall system safety.
8419	Insurance Claims Fraud Detection	AIR-Bench-Integrity	Ensuring the model can refuse manipulative or malicious requests that may tamper with data integrity is directly tied to operational security, making this metric highly relevant for preventing breaches or unintended system alterations.
10740	Code Refactoring Assistant	AIR-Bench-Integrity	The metric is highly relevant for safety as it ensures the model actively prevents actions that could undermine data integrity or system controls—an important safeguard when modifying code to maintain functionality and stability.
7688	Intelligent Customer Support Automation	AIR-Bench-Integrity	Since ensuring that the chatbot consistently refuses requests that could compromise data integrity (or lead to system control overrides) is crucial for preventing security breaches, this metric is highly relevant from a safety perspective.
11208	Infrastructure Maintenance Prediction	AIR-Bench-Integrity	Ensuring robust refusal responses to potential data tampering or system control override is crucial in a utilities context, where security breaches might directly impact operational safety. The metric is highly relevant to safeguarding data integrity and operational reliability in this use case.
10258	Property Valuation	AIR-Bench-Integrity	Maintaining data integrity and preventing unauthorized modifications are critical for ensuring the trustworthiness and reliability of valuation outputs. This metric is highly relevant for safety, as it directly tests the system’s ability to avoid security threats and protect against risks that could compromise data accuracy.
7310	Programmatic Advertising Optimization	AIR-Bench-Integrity	The metric is highly relevant to safety because ensuring the system can detect and refuse requests that may lead to data tampering or unauthorized control is critical for protecting the integrity of campaign data and maintaining secure operations in a real-time ad bidding environment.
10520	API Integration Assistant	AIR-Bench-Integrity	This metric is highly relevant from a safety perspective because it directly measures the model’s ability to recognize and refuse requests that could compromise system integrity (e.g., through malware or data tampering), which is critical when generating code and managing sensitive operations like authentication in API integrations.
8624	Healthcare Resource Optimization	AIR-Bench-Integrity	Since ensuring data integrity is critical in healthcare settings to avoid unauthorized modifications that could jeopardize patient care and system reliability, the metric is highly relevant to maintaining safety in this use case.
10100	Drug Safety Monitoring	AIR-Bench-Integrity	This metric is highly relevant to the safety dimension as it directly measures the model's ability to prevent harmful manipulations (e.g., data tampering or malicious control override), which is critical for ensuring the reliable monitoring of drug safety and protecting patient outcomes.
8755	Population Health Management	AIR-Bench-Integrity	In the context of healthcare, ensuring data integrity and preventing unauthorized modifications are critical for maintaining reliable patient and public health data, making this metric highly relevant to safety aspects.
10459	Space Exploration Data Processing	AIR-Bench-Integrity	The metric is highly relevant for safety because ensuring that the system can accurately refuse manipulative or malicious requests is critical to protecting data accuracy and preventing risks that could jeopardize space mission planning and scientific research.
8334	Financial Portfolio Management	AIR-Bench-Integrity	Ensuring that the model can identify and refuse requests leading to data tampering or unauthorized control is critical in a financial context; thus, this metric is extremely relevant to safety as it helps protect sensitive financial information and uphold system integrity.
8885	Employee Performance Analytics	AIR-Bench-Integrity	Since ensuring data integrity and preventing unauthorized modifications are crucial when handling sensitive HR performance data, the metric is highly relevant from a safety perspective.
10427	Particle Physics Data Analysis	AIR-Bench-Integrity	Ensuring that the model refuses attempts that might lead to unauthorized data modification is highly relevant for maintaining data integrity, a critical safety aspect in scientific research where tampered data could lead to serious errors or false discoveries.
7742	Autonomous Defense Systems	AIR-Bench-Integrity	Given that autonomous defense systems demand rigorous resistance to manipulative or malicious inputs that could jeopardize system integrity, this metric is extremely relevant. It directly assesses the ability to refuse risky actions—critical to preventing security breaches and ensuring the system does not inadvertently compromise its own safety protocols.
9365	Legal Research Assistant	AIR-Bench-Integrity	Since the metric focuses on preventing data tampering, malware, and unauthorized system control changes, it is highly relevant for ensuring that the legal research assistant operates safely, maintaining the integrity and reliability of legal data used in practice.
8719	Patient Risk Prediction	AIR-Bench-Integrity	Ensuring that the system can refuse scenarios leading to data tampering or unauthorized control is highly relevant to healthcare safety, where maintaining data integrity is crucial to prevent harmful outcomes.
8263	Credit Risk Predictor	AIR-Bench-Integrity	Since the metric evaluates the model’s defenses against data tampering and unauthorized control—critical aspects for maintaining data integrity—it is highly relevant to ensuring that the system operates safely without compromising ethical or operational standards.
11298	Usage Pattern Analysis	AIR-Bench-Integrity	The metric is highly relevant for safety as it measures the model's capability to refuse operations that might lead to unauthorized data tampering or security breaches, ensuring the integrity and trustworthiness of the system in a critical infrastructure domain.
10046	Drug Discovery Acceleration	AIR-Bench-Integrity	Ensuring robust data integrity is critical in pharmaceutical contexts to prevent unauthorized modifications, which could jeopardize drug development and patient safety. The metric is highly relevant to safety as it measures the model’s ability to resist security attacks that might compromise its operation.
8836	Employee Engagement Analysis	AIR-Bench-Integrity	Since the metric assesses refusal of high-risk security scenarios, it is highly relevant to ensuring that the system protects sensitive employee data and maintains safe operational integrity, which is critical in Human Resources applications.
10962	Autonomous Vehicle Control	AIR-Bench-Integrity	Ensuring that the AI can identify and block malicious or integrity-compromising scenarios is critical to avoiding potentially catastrophic failures in safety-critical systems like autonomous vehicles, making the metric extremely relevant for safety assurance.
11260	Smart Grid Management	AIR-Bench-Integrity	Given that unauthorized modifications or data tampering in a smart grid environment could lead to catastrophic outcomes, the model’s ability to reliably refuse such requests is critical, making this metric extremely relevant for ensuring overall system safety and integrity.
9061	Automated Knowledge Base Maintenance	AIR-Bench-Integrity	Since safeguarding data integrity is a critical safety concern—especially in preventing malicious modifications that could compromise the knowledge base—the metric is extremely relevant for evaluating the model’s safety performance.
10610	Bug Detection and Fixing	AIR-Bench-Integrity	Ensuring that a bug detection and fixing system does not introduce security vulnerabilities (such as malware insertion or data tampering) is critical; the AIR-Bench-Integrity metric directly assesses the model's refusal behavior against such risks, making it extremely relevant from a safety perspective.
9490	Route Optimization	AIR-Bench-Integrity	Maintaining data integrity is crucial for a route optimization system because any unauthorized modifications can lead to severe operational hazards; hence, evaluating the model's refusal of security-risk scenarios is highly relevant to ensuring system safety and protecting against malicious data tampering.
10186	Building Performance Analysis	AIR-Bench-Integrity	The metric is highly relevant for safety since it tests the model’s capacity to refuse requests that could compromise data integrity and system controls, which is critical for preventing harmful outcomes in domains like building operations and maintenance.
7890	Military Training Simulation	AIR-Bench-Integrity	The metric is highly relevant for safety because it gauges how well the model defends against unauthorized modifications and security risks—a critical consideration in a defense setting where any breach could jeopardize system integrity and lead to harmful outcomes. This focus on refusing unsafe requests aligns strongly with the need to maintain robust, tamper-resistant simulation environments.
10305	Smart Building Management	AIR-Bench-Integrity	Given that Smart Building Management involves controlling critical systems (HVAC, lighting, security), a robust refusal mechanism against data tampering and security breaches is paramount for ensuring safety and protecting system integrity.
10328	Climate Modeling	AIR-Bench-Integrity	The metric is highly relevant from a safety perspective because ensuring data integrity and preventing unauthorized modifications are crucial in maintaining the reliability of climate predictions, which are used for disaster preparedness and policymaking.
9240	Contract Analysis	AIR-Bench-Integrity	Since contract analysis often involves highly sensitive legal documents, ensuring that the system resists requests that could compromise data integrity is crucial; thus, the metric is highly relevant for assessing the system’s safety in preventing data tampering or unauthorized modifications.
11165	Energy Grid Optimization	AIR-Bench-Integrity	The metric is critical for ensuring that the AI system maintains data integrity and avoids security breaches—factors that are paramount in preventing operational failures or safety risks in modern, interconnected energy grid environments.
11128	Traffic Management	AIR-Bench-Integrity	Ensuring data integrity and preventing unauthorized control are critical in traffic management systems to avoid dangerous malfunctions; thus, a metric that measures refusal to engage in risky operations is extremely relevant to operational safety.
8681	Medical Image Analysis	AIR-Bench-Integrity	Ensuring data integrity and preventing unauthorized modifications are critical in healthcare systems, so a metric that tests refusal behavior toward security risks is highly relevant to safeguarding patient data and maintaining operational safety in medical image analysis.
7584	Automated Email Triage and Response	AIR-Bench-Integrity	The metric is highly relevant since it specifically measures safeguards against requests that could compromise data integrity or trigger security breaches, a key concern for systems handling sensitive customer communications.
9988	Clinical Trial Optimization	AIR-Bench-Integrity	Ensuring data integrity is critical in clinical trials where tampering or unauthorized modifications could lead to disastrous outcomes, making the ability to refuse risky scenarios highly pertinent. The metric’s assessment of resistance to security risks and manipulation is clearly relevant to maintaining ethical and safe operations in a sensitive healthcare context.
9909	Content Recommendation Engine	AIR-Bench-Integrity	This metric is highly relevant from a safety standpoint as it ensures that the system can identify and block security risks that might compromise data integrity, an essential factor for protecting both system operations and user trust.
9389	Litigation Prediction	AIR-Bench-Integrity	Since maintaining data integrity and preventing unauthorized data or control modifications are critical in any sensitive application (including legal settings), this metric is highly relevant for ensuring the model adheres to robust safety and ethical guidelines.
9102	Expertise Location System	AIR-Bench-Integrity	Since the metric evaluates the model’s capacity to detect and refuse actions that could compromise data integrity or system controls, it is highly relevant for ensuring the safe operation of the system.
10560	Automated Code Review	AIR-Bench-Integrity	The metric is highly relevant to safety as it explicitly evaluates the model's ability to refuse actions that could compromise data integrity and system security—a critical facet when ensuring that code reviews do not inadvertently introduce or overlook vulnerabilities.
7789	Military Intelligence Analysis	AIR-Bench-Integrity	The metric is highly relevant for safety as it directly tests the model’s ability to recognize and refuse potentially harmful requests that could compromise data and system integrity, which is critical in a military intelligence context.
9250	Legal Document Analysis	AIR-Bench-Integrity	The metric is highly relevant for safety as it assesses the model's capacity to spot and refuse requests that could lead to unauthorized modifications or attacks, which is critical when handling sensitive legal data.
8530	Tax Compliance Advisor	AIR-Bench-Integrity	The metric is highly relevant for assessing the model’s ability to avoid behaviors that could compromise data integrity and system controls, which is critical in a tax compliance context where accuracy and resistance to manipulation are essential for safe operation.
9969	Real-time Content Moderation	AIR-Bench-InfluencingPolitics	Ensuring that the model can reliably avoid engaging in political influence is critical to prevent misuse and unintended harm, making it highly relevant from a safety perspective in a real-time content moderation system.
9949	Real-time Content Moderation	AIR-Bench-IllegalServicesExploitation	Since the metric focuses on preventing the facilitation of harmful criminal activities like human trafficking and exploitation, it strongly contributes to a safe operational environment—key for protecting platform users and reducing harmful content in real-time moderation contexts.
10861	Content Moderation	AIR-Bench-IllegalServicesExploitation	The metric is extremely relevant on the safety dimension since it directly assesses the model’s ability to avoid facilitating harmful and illegal activities—a core requirement for safe content moderation in reducing toxic and harmful content.
9305	Legal Reasoning Assistant	AIR-Bench-IllegalServicesExploitation	The metric is highly relevant for safety because ensuring the model refuses to facilitate human trafficking, sexual exploitation, and other illegal services is critical to preventing harm and maintaining ethical boundaries in a legal context.
10856	Content Moderation	AIR-Bench-IllegalRegulatedSubstances	Ensuring that the model reliably refuses to facilitate illegal substance distribution or production is highly pertinent to safety, as it directly relates to preventing harmful and legally problematic outputs. This metric, while narrow in focus, aligns well with a critical safety dimension of content moderation by mitigating risks related to the promotion of illegal activities.
10054	Drug Discovery Acceleration	AIR-Bench-IllegalRegulatedSubstances	Ensuring that the model properly refuses requests involving illegal or regulated substances is crucial for maintaining ethical and legal boundaries, making this metric highly relevant for safeguarding the model’s operation within the sensitive pharmaceutical domain.
8326	Financial Portfolio Management	AIR-Bench-HighRiskFinancialActivities	The metric is highly relevant for safety because ensuring the model refuses to facilitate harmful or predatory financial activities (e.g., gambling and payday lending) is crucial for protecting clients and maintaining ethical financial advice.
7594	Automated Email Triage and Response	AIR-Bench-HateSpeech	Ensuring that the system avoids generating hate or toxic content in responses is critical for ethical and harm-free customer communications, making this metric highly relevant to the safety aspects of the use case.
9894	Content Recommendation Engine	AIR-Bench-HateSpeech	Ensuring that the system refuses to generate or surface hate speech is critical for preventing harmful content, making this metric extremely relevant for assessing the safety aspects of the use case.
10842	Content Moderation	AIR-Bench-HateSpeech	Given that managing hate speech is critical to ensuring a safe and non-toxic online environment, this metric is extremely relevant as it measures the model’s ability to avoid producing or facilitating harmful content, directly addressing ethical and bias-related safety concerns.
8803	AI-Powered Recruitment	AIR-Bench-HateSpeech	Since the recruitment tool must avoid generating or endorsing biased or harmful content, a metric that tests the model’s ability to refuse hate speech is highly relevant for ensuring ethical and safe operation in a hiring context.
7691	Intelligent Customer Support Automation	AIR-Bench-HateSpeech	Ensuring that the chatbot does not generate or facilitate hate speech is critical for preventing harm and maintaining ethical standards in customer interactions, making the metric highly relevant to the safety dimension.
8178	Personalized Tutor	AIR-Bench-HateSpeech	Preventing hate speech is critical in educational settings where the impact on diverse and impressionable audiences is significant; thus, this metric is extremely relevant to ensuring the system operates safely and ethically.
9942	Real-time Content Moderation	AIR-Bench-HateSpeech	Given that preventing hate speech is central to avoiding harm and ensuring ethical compliance in content moderation, this metric is extremely relevant as it provides direct insights into the model’s ability to maintain safety standards.
7721	Virtual Customer Service Agent	AIR-Bench-HateSpeech	Ensuring that the agent reliably refuses to generate hate speech is highly important for maintaining a safe and respectful customer environment. This metric directly evaluates a key safety aspect even though it isn’t exclusively tailored to the customer service context.
7580	Automated Email Triage and Response	AIR-Bench-Harassment	Given that automated communications in customer service must handle potentially hostile or abusive content responsibly, the ability to identify and refuse harassing language is highly relevant to ensuring the system does no harm and adheres to ethical guidelines.
8180	Personalized Tutor	AIR-Bench-Harassment	Preventing hate and harassment is critical in educational settings, where negative interactions can have a significant impact. This metric is highly relevant as it gauges the model's ability to avoid abusive behavior and uphold a safe and respectful learning environment.
7653	Intelligent Customer Support Automation	AIR-Bench-Harassment	Ensuring that the model effectively refuses to engage in or propagate harassment is critical in a customer support context to prevent harm, maintain respectful interactions, and uphold ethical guidelines.
9947	Real-time Content Moderation	AIR-Bench-Harassment	By assessing the model’s response to a range of harmful behaviors (bullying, insults, trolling, etc.), this metric is extremely relevant for ensuring that the system adheres to ethical guidelines and prevents the spread of toxic content, which is critical for user safety.
7727	Virtual Customer Service Agent	AIR-Bench-Harassment	Since the metric directly evaluates the model’s capacity to avoid engaging in harmful, harassing, or toxic interactions, it is critically important for ensuring that customer-facing agents operate safely, adhere to ethical guidelines, and maintain a respectful interaction environment.
10855	Content Moderation	AIR-Bench-Harassment	The metric is extremely relevant to safety since it directly tests the model’s ability to avoid engaging in or propagating targeted harassment and toxic behavior, which is critical for ensuring a safe, non-abusive online environment.
8838	Employee Engagement Analysis	AIR-Bench-Harassment	This metric is highly relevant for an HR application because ensuring that the system does not facilitate or engage in harassment is critical to avoid harm, bias, and unethical interactions in the workplace, even though its context is not a perfect match.
8371	Fraud Detection in Financial Transactions	AIR-Bench-FraudulentSchemes	The metric strongly evaluates the system’s ability to avoid inadvertently facilitating deceptive or harmful financial practices, which is closely aligned with the safety goals—ensuring ethical operation and minimizing economic harm—in a fraud detection context.
8312	Financial Portfolio Management	AIR-Bench-FraudulentSchemes	The metric is highly relevant from a safety standpoint because preventing the facilitation of fraudulent schemes is crucial in ensuring ethical advice and protecting against economic harm in financial services.
7345	Programmatic Advertising Optimization	AIR-Bench-FraudulentSchemes	Ensuring the model refuses to support deceptive or harmful economic practices is crucial in an advertising context, where misuse could lead to significant societal and economic harm, making this metric highly relevant to model safety.
10863	Content Moderation	AIR-Bench-Fraud	By evaluating the model’s refusal behavior on fraudulent activities, the metric offers strong insights into its safety performance—specifically, its capacity to avoid facilitating harmful or deceptive practices—making it highly relevant to ensuring ethical and harm-averse content moderation.
8461	Insurance Claims Processing	AIR-Bench-Fraud	Ensuring the model can identify and refuse support for fraudulent or deceptive activities is a critical safeguard in a financial services context, directly supporting ethical standards and reducing risks related to harmful or manipulative outputs.
8543	Tax Compliance Advisor	AIR-Bench-Fraud	Ensuring that the model reliably refuses fraudulent or deceptive requests is critical for preventing harmful misuse, making this metric highly relevant for safety in a tax compliance context where adhering to legal and regulatory standards is essential.
8376	Fraud Detection in Financial Transactions	AIR-Bench-Fraud	The metric is highly relevant from a safety standpoint because ensuring the model can reliably refuse to facilitate fraudulent or deceptive actions helps prevent the inadvertent support of harmful behavior. This aligns well with protecting users and upholding ethical guidelines, which is critical in the financial services context.
9944	Real-time Content Moderation	AIR-Bench-Fraud	The metric is highly relevant for safety as it explicitly measures the model’s ability to refuse engagement with deceptive and fraudulent activities, a key factor in ensuring that harmful and toxic content (like scams and impersonation) does not reach end users.
7717	Virtual Customer Service Agent	AIR-Bench-Fraud	Refusing to facilitate fraudulent or deceptive activities is crucial for maintaining customer trust and preventing harm, making this metric highly relevant for assessing the model’s adherence to ethical guidelines and safe practices in customer interactions.
10689	Code Generation Assistant	AIR-Bench-Fraud	While not directly tied to code generation, ensuring the model can identify and refuse to assist with fraudulent activities is crucial for safe operation and preventing misuse in a software development context.
9283	Legal Document Analysis	AIR-Bench-Fraud	Since the metric assesses a model’s ability to avoid supporting fraudulent or deceptive activities, it is highly relevant for ensuring that the legal document analysis system operates ethically and does not inadvertently facilitate harmful actions or legal missteps.
7676	Intelligent Customer Support Automation	AIR-Bench-Fraud	Ensuring that the model can effectively refuse to engage in fraud and deceptive behaviors is highly relevant for preventing harm in a public-facing support system. Although the metric is not tailored exclusively for customer support, its insights into handling deceptive requests are important for maintaining ethical and secure interactions.
7335	Programmatic Advertising Optimization	AIR-Bench-Fraud	Since programmatic advertising can be targeted by or inadvertently facilitate fraudulent behavior, a metric that tests the refusal of deceptive actions is highly relevant for ensuring the system operates safely and ethically.
7592	Automated Email Triage and Response	AIR-Bench-Fraud	The metric is highly relevant for safety because it ensures that the model can effectively identify and refuse outputs that contribute to fraud or deception, which is crucial to prevent malicious misuse even in an automated customer service context.
8418	Insurance Claims Fraud Detection	AIR-Bench-Fraud	Since the metric assesses the model’s ability to refuse assisting in fraud and deception, it is highly relevant in ensuring that the system does not inadvertently support or enable fraudulent behavior, thus safeguarding against misuse and ethical risks.
8300	Financial Portfolio Management	AIR-Bench-Fraud	Ensuring that the model effectively refuses to facilitate fraudulent activities is highly relevant for safeguarding financial decisions and preventing potential harm, thereby contributing significantly to the overall safety of the system.
8191	Personalized Tutor	AIR-Bench-Erotic	Although the metric targets a niche area of content moderation, ensuring strict boundaries around sexual content is critical in an educational context (especially with minors), making it highly relevant from a safety and ethical standpoint.
9946	Real-time Content Moderation	AIR-Bench-EndangermentHarmOrAbuseOfChildren	Child safety is a paramount concern in moderating user-generated content; the metric directly targets this risk by evaluating the model's refusal of harmful content involving children, making it extremely relevant for ensuring safe and ethical operations.
10846	Content Moderation	AIR-Bench-EndangermentHarmOrAbuseOfChildren	The metric directly evaluates the model’s ability to safeguard against extremely sensitive and high-risk content related to child endangerment and abuse, making it essential for ensuring ethical and safe content moderation practices.
8179	Personalized Tutor	AIR-Bench-EndangermentHarmOrAbuseOfChildren	Since the tutoring system will interact with minors, ensuring robust refusals of harmful or exploitative content is critical, making this metric extremely relevant to safeguarding children.
10841	Content Moderation	AIR-Bench-DisruptingSocialOrder	Since the metric evaluates the model’s ability to refuse content that could harm social order—a key safety concern—it is highly relevant for assessing safe behavior, although its China-specific focus may constrain its universality.
9025	Workforce Planning and Analytics	AIR-Bench-DisempoweringWorkers	The metric is highly relevant from a safety standpoint because it directly assesses whether the model can reliably avoid outputs that might contribute to harmful labor practices, thereby ensuring that the system upholds ethical standards and protects worker rights in an HR context.
8848	Employee Engagement Analysis	AIR-Bench-DisempoweringWorkers	The metric is highly relevant on the safety dimension because ensuring that the model refuses to contribute to labor exploitation or undermine worker rights is critically important in an HR context, where ethical considerations and the avoidance of harmful practices are paramount.
9570	Warehouse Automation	AIR-Bench-DisempoweringWorkers	Since the use case directly impacts warehouse workers and may involve changes to job quality or surveillance, a measure of the model’s stance on disempowering workers is highly relevant to ensuring the system avoids harmful practices. Hence, this metric provides significant safety insights related to worker rights and economic harm.
9627	Assembly Line Optimization	AIR-Bench-DisempoweringWorkers	Since the assembly line optimization use case involves people such as factory workers and managers who could be negatively impacted by unethical labor practices, this metric is highly relevant for ensuring that the AI does not contribute to harmful practices like worker exploitation or undue surveillance, even though it is a general test of refusal behavior.
8893	Employee Performance Analytics	AIR-Bench-DisempoweringWorkers	The metric is highly relevant for ensuring that the HR analytics tool does not inadvertently generate or support outputs that could harm workers’ rights or lead to exploitation, directly addressing ethical and worker protection concerns.
10218	Construction Project Planning	AIR-Bench-DisempoweringWorkers	Since the construction industry involves a significant number of workers, ensuring the model does not contribute to labor exploitation or undermine worker rights is critical, making the metric highly relevant from a safety and ethical standpoint.
8073	Design Trend Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the model refrains from discrimination is highly relevant for safety; even in design trend analysis, avoiding biased or harmful categorization is important for ethical and legal compliance.
9304	Legal Reasoning Assistant	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Given the legal domain’s sensitivity to bias and equal treatment, a metric that rigorously tests refusal of discriminatory activities is extremely relevant for ensuring the model operates ethically and avoids harmful misclassifications.
9086	Expertise Location System	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since the metric directly evaluates the model’s ability to refuse discriminatory practices—critical for ensuring ethical treatment in HR-related decisions—it is highly relevant for mitigating risks of biased recommendations in the expertise location system.
11130	Traffic Management	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In public systems like traffic management, it is critical to avoid bias—especially in decisions that could affect emergency response or service allocation—so a metric that gauges refusal to engage in discriminatory practices is highly relevant from a safety and ethical standpoint.
7564	Automated Email Triage and Response	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since the use case involves automated responses that affect diverse groups, ensuring the model avoids discriminatory behavior is crucial, making this metric highly relevant for assessing safety aspects.
9896	Content Recommendation Engine	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the AI does not engage in discriminatory activities is critical for content recommendation systems, especially in avoiding bias that could lead to unfair treatment of users, making this metric extremely relevant to safeguard ethical and legal standards.
7381	Social Media Campaign Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric directly tests the model’s ability to avoid harmful discriminatory behavior and biased classifications, which is critical to ensuring ethical and safe operation in any system, including one analyzing social media campaigns.
9060	Automated Knowledge Base Maintenance	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since the metric is specifically designed to evaluate the model’s capacity to recognize and refuse engagement in discriminatory practices, it is highly relevant to ensuring that the automated maintenance system produces content that adheres to ethical standards and avoids harmful biases.
8579	Diagnostic Support System	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In the healthcare context, ensuring the model does not facilitate discriminatory practices is crucial, making this metric highly relevant to its safe operation and the ethical treatment of individuals based on protected characteristics.
8929	Resume Screener	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	This metric is extremely relevant for safety because it rigorously tests the model’s ability to avoid discriminatory practices, which is crucial in HR applications to ensure fair treatment and adherence to legal and ethical standards.
10013	Clinical Trial Optimization	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In clinical trials, avoiding discriminatory practices is paramount, and this metric provides critical insights into the model’s ability to safely operate with respect to fairness and bias, making it extremely relevant for safety assessments.
9355	Legal Research Assistant	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring the model refuses to engage in discriminatory activities is highly relevant in the legal domain, where fairness and compliance with ethical standards are critical to avoid legal and rights-related risks.
9384	Litigation Prediction	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since legal applications must operate without bias or discrimination, especially when they potentially affect legal outcomes and client treatment, this metric is extremely relevant for ensuring ethical behavior and risk mitigation in the legal domain.
7749	Autonomous Defense Systems	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the system correctly refuses to engage in or propagate discriminatory practices is critically important in defense applications, where biased decision-making could lead to unjust targeting and legal/ethical violations.
7297	Marketing Attribution Modeling	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since avoiding bias and discriminatory practices is critical in any system interacting with diverse user groups—even in marketing attribution—the metric is highly relevant for ensuring that the model adheres to ethical guidelines and minimizes legal or rights‐related risks.
8186	Personalized Tutor	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring the tutoring system does not engage in discriminatory behavior or unfairly categorize students is critical, making this metric extremely relevant for assessing the system’s adherence to ethical guidelines and safe interactions.
8878	Employee Performance Analytics	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric directly evaluates the system’s ability to avoid discriminatory classifications and biased behavior, which is critical in HR applications to ensure fair treatment and adherence to ethical and legal standards.
8679	Medical Image Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the model does not engage in potentially discriminatory practices is important in healthcare, where biased outputs can harm patient care; this metric is highly relevant for assessing ethical safeguards and avoiding bias in critical, sensitive applications.
9253	Legal Document Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the model refuses participation in discriminatory classification is critically important in legal applications to avoid bias and uphold ethical and legal standards, making this metric highly relevant from a safety perspective.
9930	Real-time Content Moderation	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric is directly aligned with assessing and mitigating bias and discrimination risks, which is critical for ensuring safe and ethical operation in content moderation contexts.
8839	Employee Engagement Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In a human resources context, especially one involving employee sentiment and engagement, avoiding biased or discriminatory outputs is critical; thus, a metric that assesses the model’s capability to refuse to engage in discriminatory practices is extremely relevant for ensuring safe and ethical operations.
8231	Student Performance Prediction	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the model does not engage in or propagate bias—especially related to protected characteristics—is critical in an educational setting, making this metric highly relevant for assessing and safeguarding fair, non-discriminatory decisions in early intervention and resource allocation.
8528	Tax Compliance Advisor	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Given that tax decisions must be free from bias to avoid legal repercussions and ensure fairness, the metric is highly relevant for safety as it verifies that the model properly avoids facilitating discrimination or biased classifications.
11065	Public Transportation Optimization	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric is highly relevant for safety as it assesses the model's ability to refrain from enabling bias or discrimination—an essential consideration for ensuring fairness and ethical practices in a system that directly affects diverse populations.
9169	Knowledge Discovery and Mining	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric is highly relevant for ensuring that the system avoids facilitating discriminatory practices, which is essential for maintaining ethical standards and avoiding harm, especially in environments handling sensitive organizational data.
7811	Military Intelligence Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In the defense context, ensuring that the AI system avoids discriminatory classifications and biased decision-making is critical to prevent ethical and legal harms. The metric provides significant insight into the system’s adherence to non-discrimination principles and ethical guidelines.
10845	Content Moderation	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	This metric is extremely relevant from a safety standpoint as it rigorously tests the system’s ability to avoid engaging in discriminatory practices and mischaracterizing individuals along protected characteristics, which is critical for ethical and harm-free content moderation.
8149	Curriculum Design	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the curriculum design system operates without discriminatory biases is crucial to maintaining fairness and ethical standards in educational contexts. This metric is highly relevant for evaluating a model’s safety in terms of preventing discriminatory practices, which is a key aspect of deploying AI in education.
7878	Military Training Simulation	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	A high performance on this metric is relevant from a safety perspective, ensuring the system avoids discriminatory or biased actions—even if within military training contexts—which is critical for ethical and legally compliant operations.
10395	Genomic Research Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since the metric specifically tests for refusals in discriminatory contexts—including genetic information as a protected characteristic—it is extremely relevant for ensuring that the model does not inadvertently contribute to biases or unethical classifications, which is critical when handling sensitive genomic data.
8722	Patient Risk Prediction	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring the model does not engage in or propagate discriminatory practices is critical in healthcare, where biased predictions can lead to unethical treatment; thus, this metric is extremely relevant for safely applying the AI system.
8601	Healthcare Resource Optimization	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In the healthcare context, avoiding bias—especially regarding protected characteristics—is crucial to maintain ethical standards and prevent harm, making this metric extremely relevant for ensuring the model operates safely and equitably.
7681	Intelligent Customer Support Automation	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	This metric directly measures the model's capacity to refuse discriminatory behavior and biased classifications, a critical concern for ensuring ethical and legally compliant interactions in a customer support setting.
8284	Credit Risk Predictor	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric directly targets issues of bias and discrimination, which are critical in financial services to avoid unethical or unjust lending practices; thus, it is extremely relevant for ensuring the model operates safely and adheres to ethical guidelines.
10955	Personalized Product Recommendations	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since ensuring non-discriminatory practices is critical to avoid harm and ethical violations in customer interactions, this metric is extremely relevant for assessing the model’s ability to operate safely with respect to bias and protection of sensitive characteristics.
9831	Audience Analytics and Insights	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the model correctly refuses to engage in discriminatory classification or biased treatment is critical for ethical audience targeting and fair content strategies, making this metric highly relevant for the safety dimension.
8410	Insurance Claims Fraud Detection	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the insurance fraud detection system does not rely on or propagate biases is critical, making this metric extremely relevant for assessing and maintaining ethical safety standards.
8749	Population Health Management	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that models do not engage in biased or discriminatory behavior is critical in healthcare, where decisions can deeply impact vulnerable populations; hence, this metric is extremely relevant and provides critical safety insights for maintaining ethical standards.
8480	Insurance Policy Pricing Optimization	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric is extremely relevant for safety as it ensures that the AI system does not engage in discriminatory practices, which is critical for maintaining ethical standards and compliance in insurance pricing.
8449	Insurance Claims Processing	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In an insurance claims context, avoiding bias is critical to prevent wrongful discrimination in benefits, fraud investigation, and customer interactions, making this metric extremely relevant for ensuring safe, ethical operations.
8972	Workflow Attrition Estimator	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since HR applications are highly sensitive and impact protected groups, a metric that evaluates refusal to engage in discrimination is extremely relevant to ensuring the system operates ethically and without bias, directly addressing legal and rights-related risks.
9011	Workforce Planning and Analytics	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	In the HR context, ensuring the model avoids bias and does not facilitate discriminatory practices (especially regarding protected characteristics) is critical, making this metric extremely relevant for assessing the system’s compliance with legal, ethical, and fairness requirements.
8793	AI-Powered Recruitment	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric is extremely relevant for safety since it directly measures the model’s ability to avoid engaging in discriminatory behavior, which is critical in ensuring ethical recruitment practices and reducing bias in hiring decisions.
11304	Usage Pattern Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since utility services impact a wide range of consumers, ensuring that the model does not propagate unfair or harmful discriminatory practices is critical. The metric’s focus on avoiding unlawful bias toward protected characteristics is highly relevant in guarding against ethical, legal, and social risks in service delivery.
7317	Programmatic Advertising Optimization	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric directly evaluates how well the model can refuse to engage in discriminatory practices, making it critically important for ensuring that the system avoids bias and adheres to ethical and legal standards in sensitive contexts.
10077	Drug Safety Monitoring	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the model avoids discriminatory actions concerning protected characteristics is critical in any context, particularly in healthcare where bias can lead to harmful decisions and unequal treatment of patient subgroups, making this metric highly relevant to safety.
7251	Customer Segmentation and Targeting	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since the metric specifically assesses the model's ability to avoid engaging in discriminatory or bias-related behavior—critical for ethical marketing applications—it is extremely relevant for ensuring safe and fair operations in customer segmentation and targeting.
7958	Automated Design Generation	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric is highly relevant on the safety front because it gauges the model’s capability to avoid harmful or biased outputs—an important consideration to ensure that creative outputs do not inadvertently perpetuate stereotypes or discriminatory practices even though it isn't perfectly tailored to design-specific nuances.
7704	Virtual Customer Service Agent	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring the model does not engage in discriminatory practices is critical for ethical and legal reasons in customer interactions, making this metric extremely relevant to maintaining safe, unbiased, and compliant operations.
7624	Customer Feedback Analysis	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Since ensuring that the model does not engage in discriminatory practices is critical—especially given the involvement of protected characteristics—the metric is highly relevant for the safety dimension by testing the model’s adherence to ethical guidelines and bias refusal.
10244	Property Valuation	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that a property valuation model does not use discriminatory practices or biased categorizations is crucial, making this metric highly relevant for testing the model’s safe operation and adherence to ethical guidelines.
11039	Fleet Management	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the system does not engage in discriminatory practices is critical when decisions impact drivers and maintenance staff, making this metric extremely relevant for mitigating ethical and legal risks in fleet management.
8088	Automated Assessment & Grading	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	This metric is highly relevant from a safety perspective because ensuring that the system avoids discriminatory behavior and bias toward protected characteristics is critical, especially in an educational context where fairness and ethical standards are paramount.
8367	Fraud Detection in Financial Transactions	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	Ensuring that the model does not engage in discriminatory practices is critical for fairness, ethical compliance, and legal risk mitigation, making this metric extremely relevant to the safety dimension of the use case.
8304	Financial Portfolio Management	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	The metric is extremely relevant to safety as it directly assesses the model’s ability to avoid discriminatory behavior—a critical ethical and legal requirement in financial services, ensuring the system does not cause harm by engaging in unfair or biased practices.
10871	Content Moderation	AIR-Bench-DeterringDemocraticParticipation	The metric is highly relevant from a safety standpoint as it addresses a critical harm—political manipulation and voter suppression—which is a significant societal risk in content moderation. Ensuring the system does not contribute to undermining democratic processes is an important ethical safeguard for platforms.
10852	Content Moderation	AIR-Bench-DepictingViolence	The metric is highly relevant from a safety perspective because it directly assesses whether the model can avoid generating violent and harmful content, thereby mitigating risks associated with exposure to graphic violence. It provides strong evidence of the model’s adherence to ethical guidelines concerning content safety, even though it is focused on a subset of safety concerns.
9950	Real-time Content Moderation	AIR-Bench-DepictingViolence	The metric is highly relevant to safety since it directly gauges the model’s capacity to avoid producing or endorsing violent or extremist content, which is central to protecting users and ensuring a safe content environment.
10912	Cybersecurity Threat Detection	AIR-Bench-Confidentiality	Because the metric directly evaluates the system’s ability to avoid actions that could compromise confidentiality (by refusing potentially harmful scenarios such as network intrusion or phishing), it provides highly relevant insight into the system’s safety measures and adherence to ethical security guidelines.
8752	Population Health Management	AIR-Bench-Confidentiality	Given the sensitive nature of healthcare data, maintaining confidentiality is critical, and this metric directly assesses the model’s robustness in refusing security breach scenarios that could compromise patient and public health information.
10012	Clinical Trial Optimization	AIR-Bench-Confidentiality	For clinical trials, safeguarding sensitive patient and research data is critical, so ensuring robust defenses against cybersecurity threats is highly relevant for maintaining ethical and secure operations.
10661	Code Generation Assistant	AIR-Bench-Confidentiality	Given that preventing the generation of content that could facilitate unauthorized access or harmful cybersecurity behavior is critical, this metric is extremely relevant for ensuring the safe operation of the Code Generation Assistant.
10786	Database Query Optimizer	AIR-Bench-Confidentiality	The metric is highly relevant to ensuring that the system adheres to cybersecurity principles, which is important for protecting sensitive database information and maintaining data integrity and security.
7803	Military Intelligence Analysis	AIR-Bench-Confidentiality	In this highly sensitive defense context, ensuring that the model can consistently safeguard information and refuse attempts that could lead to unauthorized data access is critical, making this metric extremely relevant for the safety dimension.
7852	Military Logistics Optimization	AIR-Bench-Confidentiality	In the defense context, maintaining information confidentiality is critical, and this metric directly tests the system’s capacity to recognize and avoid cybersecurity breaches, making it highly relevant for ensuring the safe operation of the AI system.
7907	Military Training Simulation	AIR-Bench-Confidentiality	Ensuring that the model refuses to perform actions that could compromise information confidentiality is highly important in a defense context, making the metric very relevant for mitigating potential security risks and maintaining ethical operations.
10306	Smart Building Management	AIR-Bench-Confidentiality	Since smart building systems must resist cybersecurity threats that could lead to unauthorized access or manipulation, evaluating the model’s refusal of security risk scenarios is highly relevant to ensuring the safety and integrity of the system.
8474	Insurance Policy Pricing Optimization	AIR-Bench-Confidentiality	Ensuring robust refusal of cybersecurity threats is critical for protecting confidential insurance and customer data, making the metric highly relevant to the safety dimension despite not being tailored exclusively to this use case.
7715	Virtual Customer Service Agent	AIR-Bench-Confidentiality	Ensuring that the agent can appropriately refuse scenarios that risk compromising confidentiality is crucial for maintaining safe operations, making this metric highly relevant from a security and ethical standpoint.
7683	Intelligent Customer Support Automation	AIR-Bench-Confidentiality	Ensuring that the model can refuse scenarios that might compromise information confidentiality is highly relevant for protecting sensitive customer data and operational integrity, which is critical for a customer support system.
9197	Knowledge Discovery and Mining	AIR-Bench-Confidentiality	Since the use case involves sensitive organizational data, a strong ability to refuse potentially harmful or insecure directives—thereby maintaining information confidentiality—is highly important.
11030	Fleet Management	AIR-Bench-Confidentiality	The metric is highly relevant from a safety perspective since maintaining confidentiality is critical to prevent security breaches that could compromise fleet data and operations, potentially leading to safety hazards or systemic vulnerabilities.
11160	Energy Grid Optimization	AIR-Bench-Confidentiality	Ensuring robust refusal of cybersecurity breach scenarios is vital to protecting grid integrity and confidential operational data, making this metric highly relevant for the safety dimension in a utility context.
9148	Internal Knowledge Base Search	AIR-Bench-Confidentiality	Since the internal knowledge base handles sensitive company data, ensuring the system properly refuses cybersecurity risks is crucial. This metric is highly relevant from a safety perspective, as it gauges the model’s ability to protect against unauthorized disclosures and other security threats.
9526	Supply Chain Optimization	AIR-Bench-Confidentiality	Ensuring that the system can effectively guard against cybersecurity threats is important in supply chain applications where proprietary and sensitive data are involved, making the confidentiality measure highly relevant to the overall safety of the solution.
8875	Employee Performance Analytics	AIR-Bench-Confidentiality	The metric is highly relevant for safety since employee performance analytics involves processing sensitive HR data, making robust information confidentiality and cybersecurity measures essential.
9821	Audience Analytics and Insights	AIR-Bench-Confidentiality	Ensuring the model correctly refuses actions that could lead to cybersecurity breaches is crucial for protecting sensitive user and content data, making this metric highly relevant from a safety and ethical operational standpoint.
7776	Autonomous Defense Systems	AIR-Bench-Confidentiality	Ensuring that the model correctly recognizes and refuses actions that could lead to unauthorized access or cybersecurity incidents is critical in military defense contexts, making this metric extremely relevant for safety.
9578	Warehouse Automation	AIR-Bench-Confidentiality	Given that warehouse automation systems may be vulnerable to cyberattacks that could lead to unauthorized control of robots or disruptions in operations, ensuring strong cybersecurity defenses (including confidentiality) is highly relevant for preventing physical and operational harm.
9106	Expertise Location System	AIR-Bench-Confidentiality	Since the Expertise Location System handles sensitive employee data and internal project information, strong defenses against cybersecurity risks are critical. Measuring the model’s ability to refuse potentially hazardous scenarios is highly relevant for ensuring system security and protecting confidential information.
8617	Healthcare Resource Optimization	AIR-Bench-Confidentiality	Ensuring confidentiality and preventing unauthorized information access are critical in healthcare, so evaluating the model’s resistance to security risks is highly relevant to protecting sensitive patient and operational data.
10517	API Integration Assistant	AIR-Bench-Confidentiality	The metric is highly relevant for safety because it assesses the model’s awareness and handling of cybersecurity risks, which is crucial for preventing vulnerabilities in API integrations. Its focus on maintaining confidentiality and resisting risky scenarios directly supports safe operation in the software development context.
11259	Smart Grid Management	AIR-Bench-Confidentiality	Ensuring information confidentiality and resisting cyber threats are critical for smart grids, where a security breach can lead to widespread system failures and harm. This metric directly evaluates the model’s safety-related ability to handle cybersecurity risks, making it extremely relevant for safe grid management.
8434	Insurance Claims Processing	AIR-Bench-Confidentiality	The metric is highly relevant to safety because it assesses the model’s mechanisms for refusing actions that could lead to security breaches and unauthorized data access, a critical aspect in handling sensitive insurance data and ensuring operational integrity.
8791	AI-Powered Recruitment	AIR-Bench-Confidentiality	Since the recruitment system handles sensitive personal information, a strong ability to refuse unauthorized or insecure requests is highly relevant for protecting confidentiality and preventing data breaches, making this metric notably pertinent from a safety perspective.
10992	Autonomous Vehicle Control	AIR-Bench-Confidentiality	The ability to recognize and refuse scenarios that could lead to unauthorized system access is highly relevant to the safety of autonomous vehicles, as preventing cybersecurity vulnerabilities is critical to avoid potential real-world accidents and system hijacks.
8316	Financial Portfolio Management	AIR-Bench-Confidentiality	Ensuring that a model can recognize and refuse requests that may lead to cybersecurity breaches is highly relevant in safeguarding sensitive financial data and maintaining client confidentiality. This metric is therefore critical in a financial context, where unauthorized access or data compromise could have severe consequences.
10836	Content Moderation	AIR-Bench-ChildSexualAbuse	This metric is extremely relevant to safety, as it directly measures the model’s adherence to ethical guidelines by testing its ability to strictly refuse content that poses severe harm. It acts as a near-direct proxy for ensuring safety against content related to child sexual abuse, a critical aspect of content moderation safety.
8195	Personalized Tutor	AIR-Bench-ChildSexualAbuse	Although the metric is narrowly focused on child sexual abuse content, ensuring strict refusal in this area is highly relevant for protecting a vulnerable audience in an educational setting.
9941	Real-time Content Moderation	AIR-Bench-ChildSexualAbuse	Given that preventing the dissemination of child sexual abuse content is critical to ensuring user safety and ethical standards, the metric is extremely relevant as it directly measures the model’s ability to avoid generating or permitting severely harmful content.
9921	Content Recommendation Engine	AIR-Bench-ChildSexualAbuse	Ensuring strict boundaries against child sexual abuse content is highly relevant to safety in a media context, as it helps prevent the inadvertent promotion or exposure to harmful material, making this metric a significant safety indicator for the system.
10838	Content Moderation	AIR-Bench-CelebratingSuffering	The metric is extremely relevant from a safety perspective since it evaluates the model’s refusal to endorse or propagate harmful narratives, ensuring that the system robustly prevents the normalization of violence and extremism, which is critical in protecting community well-being.
7897	Military Training Simulation	AIR-Bench-CelebratingSuffering	Ensuring that the model does not glorify or minimize suffering is crucial in a military training context, where realistic yet ethically restrained portrayals of violence are required, making this metric highly relevant for safe and responsible operation.
9890	Content Recommendation Engine	AIR-Bench-CelebratingSuffering	The metric is highly relevant for safety because it directly evaluates the model's ability to avoid glorifying or downplaying violent content, which is critical for maintaining ethical content recommendations and protecting users from harmful narratives.
7771	Autonomous Defense Systems	AIR-Bench-CelebratingSuffering	Ensuring that the system does not normalize or glorify violent acts is highly relevant to ethical and operational safety in a defense context, helping to prevent harmful biases and inappropriate responses, which is critical for safe deployment in sensitive military environments.
9306	Legal Reasoning Assistant	AIR-Bench-CelebratingSuffering	Since legal applications must maintain ethical boundaries and avoid inadvertently promoting harmful content, the model’s ability to refuse glorification or minimization of violence is highly relevant, making this an important safety measure.
7808	Military Intelligence Analysis	AIR-Bench-CelebratingSuffering	In a military intelligence context, ensuring that the AI does not inadvertently legitimize or glorify violence is critical to ethical operation, making this metric highly relevant for evaluating harmful content risks in outputs.
9966	Real-time Content Moderation	AIR-Bench-CelebratingSuffering	The metric is critically relevant to safety since it ensures that the model does not contribute to the normalization or celebration of harmful, violent content, thereby protecting vulnerable users and maintaining ethical content standards.
10091	Drug Safety Monitoring	AIR-Bench-Availability	Ensuring system availability is crucial in contexts like drug safety monitoring, where downtime or disruptions could delay critical adverse event detection; thus, the metric’s focus on refusing harmful requests provides valuable safety insights related to operational resilience.
11131	Traffic Management	AIR-Bench-Availability	Ensuring the system remains available despite security threats is critical in a traffic management context, where service disruptions can have severe safety implications; thus, the metric is highly relevant for assessing the resilience and ethical operation of the system.
11173	Energy Grid Optimization	AIR-Bench-Availability	Ensuring that the model can decline requests that might compromise system availability is highly relevant to the energy grid context, where operational stability and security are critical to prevent outages or disruptions.
8590	Diagnostic Support System	AIR-Bench-Availability	Ensuring the model refuses requests that could impair system availability is important for a healthcare diagnostic system, as uninterrupted service is critical to avoid potential harm to patients in high-risk settings.
10699	Code Generation Assistant	AIR-Bench-Availability	Ensuring the model refuses requests that could enable denial-of-service or other security risks is highly relevant for safety, as it prevents the generation of potentially harmful or misused code that could impair systems.
7888	Military Training Simulation	AIR-Bench-Availability	Ensuring that the system reliably refuses requests that might compromise its availability is crucial in a military environment, making this metric highly relevant to maintaining operational safety and integrity.
7925	Asset Management and Organization	AIR-Bench-Availability	The metric is focused on ensuring that the system can identify and refuse potentially harmful security requests that could lead to service interruptions, which is critical to maintaining system integrity and protecting assets, even though the connection is indirect.
11029	Fleet Management	AIR-Bench-Availability	Ensuring the system can refuse requests that might lead to denial-of-service or network disruption is highly relevant, as maintaining system availability is critical to preventing cascading safety issues in fleet operations.
10512	API Integration Assistant	AIR-Bench-Availability	Ensuring that the model can identify and refuse requests that could lead to security risks like denial of service is highly relevant for protecting system availability, which is a critical safety concern when integrating third-party APIs.
9547	Warehouse Automation	AIR-Bench-Availability	Ensuring the model can identify and refuse requests that might compromise system availability is highly relevant for preventing operational disruptions and potential safety hazards in an automated warehouse environment.
11246	Smart Grid Management	AIR-Bench-Availability	Since maintaining system availability and preventing denial-of-service type vulnerabilities are critical for grid stability and public safety, this metric is highly relevant to assessing whether the model can safeguard against security risks in a high-stakes environment.
7516	Livestock Health Monitoring	AIR-Bench-Availability	Ensuring that the system can refuse harmful requests that might lead to denial‐of‐service or network disruption is highly relevant for safety, as any downtime could delay critical health interventions for the livestock, thereby directly impacting animal welfare.
7665	Intelligent Customer Support Automation	AIR-Bench-Availability	Ensuring that the system does not process requests that could lead to network disruptions or denial of service is crucial for maintaining uninterrupted support and protecting both the service and its users. Therefore, the metric is highly relevant to the safety dimension as it directly assesses the AI’s risk-avoidance behaviors in a live, sensitive customer service environment.
8599	Healthcare Resource Optimization	AIR-Bench-Availability	The metric is highly relevant to safety because it assesses the system's ability to mitigate risks (such as denial of service) that could lead to critical disruptions in a healthcare setting where continuous availability is essential for patient care.
7710	Virtual Customer Service Agent	AIR-Bench-Availability	The metric is highly relevant from a safety perspective because it ensures that the model avoids requests that could compromise system stability, an important aspect of maintaining secure, uninterrupted customer service operations.
7744	Autonomous Defense Systems	AIR-Bench-Availability	The metric is highly relevant for safety, as ensuring that the system can avoid actions leading to service disruption is critical in defense applications where stable and secure operations are paramount.
8737	Population Health Management	AIR-Bench-Availability	Ensuring that the system can resist and refuse requests that could compromise availability is highly relevant in a healthcare context, where system disruptions could have serious real-world consequences on patient care and public health.
7832	Military Logistics Optimization	AIR-Bench-Availability	Ensuring system availability and preventing denial-of-service or network disruptions is highly relevant for safety in a military logistics context, as continuity and reliability are critical to operational effectiveness and avoiding harmful interruptions.
8113	Automated Assessment & Grading	AIR-Bench-Availability	The metric is highly relevant for safety as it assesses the model’s ability to prevent system impairments and denial-of-service scenarios, which is crucial for maintaining uninterrupted, secure operation in an educational environment.
7794	Military Intelligence Analysis	AIR-Bench-Availability	Ensuring system availability and effective refusal of requests that could lead to denial of service are critical for maintaining secure operations, particularly in a defense context. This metric is highly relevant for safety as it helps prevent system disruptions that could compromise intelligence operations.
10312	Smart Building Management	AIR-Bench-Availability	Ensuring that the model can refuse potentially harmful requests that may compromise system availability is crucial for safe operations in a smart building, where disruption can lead to security lapses and physical risks, hence making it highly relevant.
11002	Autonomous Vehicle Control	AIR-Bench-Availability	Ensuring that the system can detect and refuse requests that would impair availability is vital for safety in autonomous vehicles, as disruptions or denial-of-service conditions could lead to hazardous situations.
10763	Database Query Optimizer	AIR-Bench-Availability	This metric is highly relevant to safety since it helps ensure that the system appropriately detects and refuses requests that could lead to denial of service or system impairment, thereby protecting the database environment’s availability and overall operational integrity.
8324	Financial Portfolio Management	AIR-Bench-Availability	The metric is highly relevant for evaluating how the system handles security risks that could impact availability—an important safety aspect to ensure the system remains reliable and does not expose clients and advisors to operational hazards.
11066	Public Transportation Optimization	AIR-Bench-Availability	The metric is highly relevant to safety as it ensures that the AI can refuse actions that may compromise system availability, an essential aspect in maintaining uninterrupted public transportation services and preventing harmful disruptions.
11300	Usage Pattern Analysis	AIR-Bench-Availability	Since utilities rely on continuous availability and service reliability, a metric that measures the model’s ability to refuse requests that could lead to system impairments is highly relevant. It ensures that the model avoids behaviors that might compromise operational integrity, reducing the risk of service disruption and related harms.
10900	Cybersecurity Threat Detection	AIR-Bench-Availability	This metric is extremely relevant from a safety perspective since it directly assesses whether the system can identify and refuse actions that could lead to denial-of-service or system impairment, which are critical in avoiding additional security risks and ensuring ethical, harm‐preventative behavior in a cybersecurity context.
9963	Real-time Content Moderation	AIR-Bench-Availability	By measuring how the system refuses requests that could impair service availability, the metric is highly relevant to ensuring a safe and reliable operation, which is crucial in a live content moderation setting.
9625	Assembly Line Optimization	AIR-Bench-Availability	Ensuring system availability by refusing potentially harmful requests is crucial in a manufacturing context where downtime or network disruptions can lead to unsafe conditions or costly operational failures. This metric is therefore highly relevant for understanding the model’s role in maintaining safe, reliable operations on the assembly line.
10698	Code Generation Assistant	AIR-Bench-AutonomousUnsafeOperationOfSystems	Although the code generation assistant is not primarily meant for controlling safety‐critical systems, it must still safely refuse requests that could lead to the autonomous operation of dangerous infrastructure, making this metric quite relevant for evaluating its safe operation.
10996	Autonomous Vehicle Control	AIR-Bench-AutonomousUnsafeOperationOfSystems	This metric directly evaluates the system’s safeguards by testing its refusal of dangerous operational commands, which is critical for ensuring that autonomous vehicles adhere to high safety standards and avoid risky behaviors.
11132	Traffic Management	AIR-Bench-AutonomousUnsafeOperationOfSystems	The metric is highly relevant to safety as it gauges whether the model can appropriately refuse to engage in unsafe control over critical transportation systems, ensuring it does not execute actions that could lead to hazardous outcomes.
11215	Infrastructure Maintenance Prediction	AIR-Bench-AutonomousUnsafeOperationOfSystems	For a system operating in critical infrastructure, ensuring that the model won’t engage in unauthorized or unsafe operations is very important, making this metric highly relevant to the safety aspect of preventing misuse in high-risk scenarios.
9739	Production Process Optimization	AIR-Bench-AutonomousUnsafeOperationOfSystems	Because the metric tests for the refusal of dangerous autonomous actions in contexts that include heavy machinery and other critical systems—areas that are directly relevant to manufacturing safety—it is highly pertinent to ensuring safe operational behavior in the production environment.
9610	Assembly Line Optimization	AIR-Bench-AutonomousUnsafeOperationOfSystems	The metric is highly relevant from a safety standpoint because assembly lines involve heavy machinery and potentially hazardous processes; ensuring the model can recognize and refuse unsafe control actions is critical to prevent operational misuses and maintain worker and system safety.
11240	Smart Grid Management	AIR-Bench-AutonomousUnsafeOperationOfSystems	Since grid management is a critical infrastructure task with significant safety risks, measuring the system's ability to prevent unsafe autonomous actions is extremely relevant and provides crucial information on operational safety.
7763	Autonomous Defense Systems	AIR-Bench-AutonomousUnsafeOperationOfSystems	This metric directly measures whether the system can reliably avoid participating in high-risk behaviors—crucial for weapon systems and battlefield management—thus making it extremely relevant to ensuring the safe operation of autonomous defense systems.
7899	Military Training Simulation	AIR-Bench-AutonomousUnsafeOperationOfSystems	The metric is highly relevant for ensuring that the system does not inadvertently attempt to engage in critical or dangerous autonomous operations—a core safety concern in any defense-related system—even if used in a simulated environment.
11149	Energy Grid Optimization	AIR-Bench-AutonomousUnsafeOperationOfSystems	Since energy grids are explicitly mentioned among the critical systems, the metric is extremely relevant for assessing whether the model can safely avoid unauthorized or dangerous interventions in operational settings, which is essential for managing safety risks in this context.
7846	Military Logistics Optimization	AIR-Bench-AutonomousUnsafeOperationOfSystems	Given that the military logistics context can directly impact high-stakes operations, having a mechanism to reliably refuse autonomous control in high-risk scenarios (including aspects of battlefield management and weapon systems) is highly relevant.
11084	Public Transportation Optimization	AIR-Bench-AutonomousUnsafeOperationOfSystems	The metric is highly relevant from a safety perspective because it ensures that the model is cautious about undertaking autonomous roles in critical, operationally sensitive environments like transportation, which aligns with preventing potentially dangerous misuses in public transit systems.
7821	Military Intelligence Analysis	AIR-Bench-AutonomousUnsafeOperationOfSystems	In a defense setting where misoperations could have catastrophic consequences, ensuring the model refuses to engage in unsafe autonomous actions is critical. This metric directly addresses the prevention of high-risk, unsanctioned activity, which is paramount for maintaining operational safety.
9581	Warehouse Automation	AIR-Bench-AutonomousUnsafeOperationOfSystems	Because warehouse automation involves managing robotics and potentially hazardous machinery, ensuring the system safely refuses unsafe commands is highly relevant; the metric’s emphasis on avoiding unauthorized or dangerous operations is well aligned with important safety concerns in this domain.
8243	Student Performance Prediction	AIR-Bench-AutomatedDecisionMaking	Because the metric assesses whether a model can avoid automated decisions that risk harming human rights or opportunities—including in education eligibility—it is highly relevant to ensuring that student performance predictions do not lead to unethical or biased interventions.
8978	Workflow Attrition Estimator	AIR-Bench-AutomatedDecisionMaking	Since the metric evaluates the model’s ability to avoid making unethical or high-risk decisions—which is critical in HR contexts where biased or harmful automation can affect employee rights—it is highly relevant to ensuring the system operates safely.
8915	Resume Screener	AIR-Bench-AutomatedDecisionMaking	The metric is highly relevant for safety because it benchmarks the model’s ability to recognize and avoid high-risk, ethically sensitive automated decisions—critical for minimizing potential bias and harm in employment-related decisions.
8805	AI-Powered Recruitment	AIR-Bench-AutomatedDecisionMaking	The metric is extremely relevant to safety for this use case because it specifically targets ethical risks in high-impact automated decisions, including employment-related outcomes such as profiling and eligibility; ensuring the model avoids harmful or biased decisions is critical in HR applications.
9006	Workforce Planning and Analytics	AIR-Bench-AutomatedDecisionMaking	Because the metric assesses the model’s ability to avoid high-risk operational misuses in sensitive domains—including employment-related decisions—it is highly relevant for ensuring that HR automated systems act ethically and without bias.
8272	Credit Risk Predictor	AIR-Bench-AutomatedDecisionMaking	The metric is highly relevant to safety because it evaluates the system’s ability to recognize and refuse decisions that could unfairly affect individuals’ financial rights, which is crucial for ethical and non-biased credit risk assessments.
8506	Insurance Policy Pricing Optimization	AIR-Bench-AutomatedDecisionMaking	The metric is highly relevant to safety because it evaluates whether the model can recognize and refuse ethically questionable decisions, which is critical in a sensitive domain like insurance where outcomes can affect eligibility and lead to discriminatory practices.
8464	Insurance Claims Processing	AIR-Bench-AutomatedDecisionMaking	The metric is quite relevant for safety because it addresses scenarios (including insurance eligibility) where automated decisions could unduly impact human rights or lead to biased outcomes, a concern that applies to sensitive decisions in insurance claims processing.
8417	Insurance Claims Fraud Detection	AIR-Bench-AutomatedDecisionMaking	Given that the metric focuses on preventing high-risk misuses—including decisions that could adversely affect insurance eligibility and harm individuals—it is highly relevant for ensuring that an insurance fraud detection system does not overstep ethical or legal boundaries when automated decisions are involved.
9321	Legal Reasoning Assistant	AIR-Bench-AutomatedDecisionMaking	Given that the legal domain is particularly sensitive—where issues like improperly automated decisions could adversely affect legal rights—the metric is highly relevant for evaluating whether the system safely avoids engaging in risky decision-making that could lead to harm.
9267	Legal Document Analysis	AIR-Bench-AutomatedDecisionMaking	By testing the model’s capacity to refuse engaging in high‐risk automated decision-making that could negatively impact legal rights, the metric is highly relevant for safety, helping ensure that the system adheres to ethical guidelines and avoids harmful automated decisions in sensitive legal contexts.
8849	Employee Engagement Analysis	AIR-Bench-AutomatedDecisionMaking	Ensuring that the model refrains from making harmful or biased automated decisions is crucial in HR applications, where decisions can significantly impact individuals’ careers and well‐being, making this metric highly relevant for safety.
8890	Employee Performance Analytics	AIR-Bench-AutomatedDecisionMaking	Because HR applications can have significant consequences on employment outcomes, ensuring safe, ethical automated decision-making (and the refusal to engage in harmful practices) is crucial, making this metric highly relevant for assessing safety in this use case.
9393	Litigation Prediction	AIR-Bench-AutomatedDecisionMaking	Given that litigation predictions carry significant legal and human rights implications, ensuring the model avoids harmful automatized decisions is crucial. This metric is highly relevant for gauging the model’s adherence to ethical guidelines and its ability to prevent risky automated decisions in sensitive legal contexts.
11237	Smart Grid Management	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Ensuring appropriate refusals in regulated domains is crucial to avoid unsafe, unlicensed, or unethical advice, making this metric highly relevant for assessing safety in a heavily regulated utility environment.
9275	Legal Document Analysis	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Given its focus on ensuring that the model adheres to professional boundaries and avoids providing unlicensed advice, this metric is extremely relevant for the legal domain where adherence to regulatory and ethical standards is critical.
8718	Patient Risk Prediction	AIR-Bench-AdviceInHeavilyRegulatedIndustries	In healthcare, ensuring that the model does not overstep into providing unlicensed medical advice is critical for preventing harm. This metric is highly relevant to safety because it tests the model’s adherence to professional and regulatory boundaries, which is essential in such a sensitive, heavily regulated industry.
9404	Litigation Prediction	AIR-Bench-AdviceInHeavilyRegulatedIndustries	The metric is highly relevant from a safety standpoint because it assesses whether the model clearly understands and respects professional boundaries and regulatory constraints, which is crucial in legal contexts where improper advice could lead to severe consequences.
10096	Drug Safety Monitoring	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Ensuring that the model avoids providing unlicensed or inappropriate professional advice is crucial in the pharmaceutical domain. This metric is highly relevant for safeguarding against potential harm and maintaining adherence to regulatory requirements in a heavily regulated industry.
8546	Tax Compliance Advisor	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Since tax advice in the realm of financial services is highly regulated, the system’s ability to refuse or limit advice where proper credentials are lacking is critical to prevent regulatory, legal, or financial harm. The metric is focused on ensuring that the AI respects professional boundaries and ethical guidelines, making it extremely relevant to safe operation within a heavily regulated environment.
10042	Drug Discovery Acceleration	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Ensuring that the AI refrains from dispensing unlicensed or inappropriate advice is highly relevant in pharmaceutical contexts where regulatory compliance is critical, helping to prevent unsafe or unethical guidance.
8680	Medical Image Analysis	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Ensuring that the system appropriately refuses to offer unlicensed or improper medical advice is very important in healthcare, making this metric highly relevant to the safety dimension; it helps confirm that the system respects professional and regulatory boundaries.
9285	Legal Reasoning Assistant	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Since legal work is heavily regulated and missteps can have significant adverse consequences, the metric’s focus on adhering to professional boundaries and regulatory requirements is highly relevant for ensuring safe operation.
8638	Healthcare Resource Optimization	AIR-Bench-AdviceInHeavilyRegulatedIndustries	In a healthcare setting, safeguarding against the risk of providing unlicensed or inappropriate professional advice is critical; ensuring that any such advice requests are properly refused is directly aligned with avoiding harm and upholding regulatory and ethical standards.
9974	Clinical Trial Optimization	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Since clinical trials occur in a heavily regulated pharmaceutical context with significant risks, a strong ability to refuse unqualified advice is crucial; this metric effectively gauges the model’s understanding of professional boundaries and adherence to safety and ethical guidelines.
8586	Diagnostic Support System	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Ensuring that the model appropriately declines to offer unlicensed or overstepping advice is critical in healthcare due to high regulatory and ethical demands, making the metric highly relevant from a safety standpoint.
9228	Contract Analysis	AIR-Bench-AdviceInHeavilyRegulatedIndustries	Since legal contract analysis can entail providing sensitive regulatory advice, this metric is highly relevant for ensuring the model avoids unqualified legal recommendations and respects professional boundaries in a heavily regulated domain.
8767	Population Health Management	AIR-Bench-AdviceInHeavilyRegulatedIndustries	In a heavily regulated field like healthcare, ensuring that the model properly refuses to offer unlicensed or inappropriate professional advice is essential for avoiding harm and maintaining ethical standards, making this metric highly relevant for safety.
9343	Legal Research Assistant	AIR-Bench-AdviceInHeavilyRegulatedIndustries	This metric is highly relevant from a safety perspective because it gauges the model’s adherence to professional and regulatory boundaries in a regulated field like law, ensuring it does not inadvertently dispense unauthorized legal advice.
8337	Financial Portfolio Management	AIR-Bench-AdviceInHeavilyRegulatedIndustries	In the financial services context, providing unlicensed or misleading advice could easily lead to regulatory violations and harm; thus, a metric measuring safe refusals is critical to ensuring the system respects stringent legal and ethical boundaries.
8199	Personalized Tutor	AIR-Bench-AdultContent	Ensuring that the model properly refuses to generate explicit sexual content is highly important in an education context, especially given the involvement of minors and the need to prevent inappropriate material, making the metric highly relevant for safety.
7703	Virtual Customer Service Agent	AIR-Bench-AdultContent	Ensuring that the agent refrains from generating explicit sexual content is important for ethical, brand, and regulatory considerations, making the metric highly relevant to the safety dimension of this use case.
9943	Real-time Content Moderation	AIR-Bench-AdultContent	By focusing on the refusal of explicit sexual content, the metric is highly relevant for safety, as it helps ensure the system avoids generating harmful or explicit material; however, it is narrowly scoped to sexual content, missing other safety dimensions in broader moderation scenarios.
10874	Content Moderation	AIR-Bench-AdultContent	Since safely handling explicit sexual content is a key aspect of moderating harmful or inappropriate material, the metric is highly relevant for assessing the model’s safety; however, it focuses solely on sexual content risks rather than the full spectrum of potential safety issues.
8184	Personalized Tutor	AIR-Bench-AcademicDishonesty	Preventing facilitation of academic dishonesty is critically important in an educational context, making this metric extremely relevant for ensuring the system adheres to ethical guidelines and minimizes harm by upholding academic integrity.
8107	Automated Assessment & Grading	AIR-Bench-AcademicDishonesty	This metric is extremely relevant to safety as it directly addresses the ethical and societal risks of facilitating cheating and plagiarism, ensuring that the system adheres to academic integrity standards and avoids harmful behavior.
4504	Real-time Content Moderation	AILuminate (Suicide & Self-Harm)	Since the metric tests the system’s ability to avoid enabling self-harm—a critical and sensitive safety concern—it is highly relevant for assessing the safe operation of real-time content moderation systems, despite its narrow focus within the wider safety spectrum.
6012	Content Moderation	AILuminate (Suicide & Self-Harm)	This metric is directly focused on preventing harmful responses related to suicide and self-harm, making it extremely relevant for assessing and ensuring the safety aspects essential in a content moderation system.
832	Virtual Customer Service Agent	AILuminate (Suicide & Self-Harm)	Ensuring that the model does not produce responses that could enable self-harm is highly important for any customer-facing system, as emotionally charged interactions may occur; even though the metric targets a narrow safety area, it is critical in protecting vulnerable users during such interactions.
1632	Personalized Tutor	AILuminate (Suicide & Self-Harm)	Given that the tutoring system may interact with vulnerable student populations, the ability to prevent responses that could encourage self-harm is critically important, making this metric extremely relevant for assessing safety.
3442	Legal Reasoning Assistant	AILuminate (Specialized Advice)	As the metric directly tests safeguards against dispensing unqualified legal advice—a highly sensitive area in the legal domain—it is extremely relevant for ensuring that the system does not inadvertently cause harm or mislead users.
3611	Litigation Prediction	AILuminate (Specialized Advice)	The metric is specifically designed to test safeguards against providing harmful or unqualified legal advice, which is crucial in a legal context where misleading guidance can have serious repercussions.
2396	Medical Image Analysis	AILuminate (Specialized Advice)	Since the use case involves medical diagnostics, ensuring that any advice or interpretation comes with proper disclaimers is critical; the metric’s focus on preventing unsafe or unqualified medical advice makes it highly relevant for gauging safety.
2240	Diagnostic Support System	AILuminate (Specialized Advice)	Given that the use case involves sensitive medical advice, a metric that specifically tests safeguards against providing potentially harmful or unqualified advice is extremely relevant for ensuring safe operation in healthcare contexts.
3518	Legal Research Assistant	AILuminate (Specialized Advice)	Because the metric specifically tests safeguards against providing potentially harmful or unqualified legal advice, it is highly relevant for ensuring that a Legal Research Assistant adheres to ethical guidelines and avoids causing harm in sensitive legal discussions.
4721	Drug Safety Monitoring	AILuminate (Specialized Advice)	Since the use case involves medical data and potential impact on patient outcomes, ensuring that the system does not dispense unsafe or unqualified medical guidance is crucial, so this safety metric is highly relevant to prevent harm.
2450	Patient Risk Prediction	AILuminate (Specialized Advice)	Because patient risk prediction directly involves sensitive medical information and potential advice, having safeguards against harmful or unqualified guidance is critical; thus, this metric is highly relevant to ensuring that the system’s outputs do not inadvertently cause harm.
2192	Tax Compliance Advisor	AILuminate (Specialized Advice)	Ensuring that the system does not dispense unqualified financial advice is critical for a tax compliance advisor, making this metric extremely relevant to maintaining safe, compliant, and ethically responsible outputs.
5016	Property Valuation	AILuminate (Specialized Advice)	Since property valuation involves financial decision‐making, ensuring that the system does not provide unqualified or potentially harmful advice is critical. The metric’s focus on preventing unsafe financial advice is highly relevant to safeguarding users in this context.
4614	Clinical Trial Optimization	AILuminate (Specialized Advice)	Because clinical trial optimization touches on sensitive medical areas, ensuring that the system avoids giving unqualified or harmful advice is crucial; this metric—though focused on specialized advice—offers a highly relevant measure of its ability to manage safety in a medical context.
2101	Insurance Policy Pricing Optimization	AILuminate (Specialized Advice)	The metric is highly relevant for assessing safety since it specifically tests that the system avoids providing unqualified financial advice—a critical requirement in financial services where incorrect recommendations can have significant harmful ramifications.
1814	Financial Portfolio Management	AILuminate (Specialized Advice)	The metric is highly targeted at preventing harmful or misleading specialized advice in sensitive areas like finance, making it extremely relevant to ensuring the system adheres to necessary ethical and safety guidelines in financial portfolio management.
2023	Insurance Claims Processing	AILuminate (Specialized Advice)	Because the metric measures safeguards against inappropriate specialized advice—a risk area in sensitive financial contexts—it is highly relevant to ensuring the system avoids harmful outputs and adheres to ethical guidelines in the insurance domain.
3314	Contract Analysis	AILuminate (Specialized Advice)	The metric is explicitly designed to test safeguards against providing unsound legal advice, which is critical in a legal setting where even slight errors can have severe consequences. Strong performance on this benchmark directly indicates that the system can responsibly handle legal content and mitigate the risk of issuing harmful or misleading advice.
3397	Legal Document Analysis	AILuminate (Specialized Advice)	Since the metric directly measures the system’s safeguards against providing inappropriate legal advice—a critical risk area in legal applications—it is highly relevant from a safety perspective in ensuring ethical and harm-averse performance.
6016	Content Moderation	AILuminate (Sexual Content)	The metric directly focuses on preventing inappropriate sexual content, a common area of harm, making it a highly relevant proxy for assessing safety measures even though it does not cover all potential safety risks in content moderation.
4531	Real-time Content Moderation	AILuminate (Sexual Content)	Since sexual content can be particularly sensitive and harmful in a live-stream context, robust safeguards in this area are critical. The metric directly measures the model’s adherence to a key safety standard, making it highly relevant for ensuring a safe user experience.
1576	Personalized Tutor	AILuminate (Sexual Content)	Ensuring that a personalized tutor does not inadvertently deliver sexual content is crucial, especially when serving minors, making this metric extremely relevant for safeguarding the system’s interactions in an educational context.
4535	Real-time Content Moderation	AILuminate (Sex-Related Crimes)	Since the metric directly measures a system’s ability to prevent content that promotes sexual violence or exploitation—a serious safety concern—it is highly relevant for ensuring ethical and safe content moderation. However, while very pertinent, it covers just one aspect of safety hazards, which is why it is not rated as the absolute maximum.
5983	Content Moderation	AILuminate (Sex-Related Crimes)	The metric directly evaluates the model’s ability to prevent dangerous content (specifically sexual violence and exploitation), making it highly relevant to ensuring that the system avoids harm in critical safety-related areas.
2650	Employee Engagement Analysis	AILuminate (Privacy)	Since employee data are inherently sensitive, a metric that rigorously evaluates the protection of such information is critically relevant, ensuring that the system does not inadvertently expose nonpublic personal details that could lead to harm.
2301	Healthcare Resource Optimization	AILuminate (Privacy)	Since the healthcare domain involves handling highly sensitive patient data, a metric that assesses protection against exposing nonpublic personal information is extremely relevant to ensuring the system operates safely and adheres to ethical and privacy guidelines.
4720	Drug Safety Monitoring	AILuminate (Privacy)	Ensuring that sensitive patient and drug safety data remains confidential is critical in the pharmaceutical domain. This metric directly evaluates protection against exposing private information, making it extremely relevant to the safety dimension of the use case.
3525	Legal Research Assistant	AILuminate (Privacy)	In a legal research context where confidential or sensitive information may be involved, robust privacy safeguards are critical, making this metric highly relevant for preventing harmful disclosures.
1660	Student Performance Prediction	AILuminate (Privacy)	Ensuring that personally sensitive data remains secure is critical in an educational context, making this metric highly relevant for assessing the system’s ability to prevent privacy breaches and protect student information.
1822	Financial Portfolio Management	AILuminate (Privacy)	The metric directly measures the system’s ability to prevent exposure of sensitive, nonpublic personal information, which is crucial in the financial services domain to protect clients from potential digital or financial harm.
3147	Internal Knowledge Base Search	AILuminate (Privacy)	Since internal knowledge bases can include sensitive HR or policy details, ensuring that the model does not inadvertently reveal sensitive or nonpublic personal information is quite important, making this privacy-focused metric highly relevant for safety in this use case.
1898	Fraud Detection in Financial Transactions	AILuminate (Privacy)	Since financial services are highly sensitive to privacy breaches—and exposing private, nonpublic information can lead to considerable harm—the metric is highly relevant for ensuring the system’s safeguards work effectively to prevent such exposures.
6311	Fleet Management	AILuminate (Privacy)	Privacy protection is critical for safeguarding personal information related to drivers and staff in fleet management, making this metric extremely relevant to ensuring the system adheres to ethical and safety standards.
3236	Knowledge Discovery and Mining	AILuminate (Privacy)	Since handling organizational documents and communications can involve sensitive personal data, a robust privacy safeguard—precisely what this metric measures—is critical for ensuring the system operates safely and ethically.
1451	Automated Assessment & Grading	AILuminate (Privacy)	Since an educational grading system handles personal data from students, teachers, and parents, ensuring the protection of sensitive information is critical; thus, this privacy-focused metric is highly relevant to the system's safety.
4293	Audience Analytics and Insights	AILuminate (Privacy)	Since audience analytics often involves personal and behavioral data, a robust privacy safeguard measure is critical, making this metric extremely relevant for ensuring the system operates without exposing sensitive information.
6705	Usage Pattern Analysis	AILuminate (Privacy)	Ensuring that consumer usage data remains secure and private is critical in utilities, so a metric that robustly assesses protection against leaking personal information is extremely relevant from a safety standpoint.
3085	Expertise Location System	AILuminate (Privacy)	Since the system deals with potentially sensitive employee data, a metric that evaluates safeguards against exposing personal or private information is highly relevant to ensuring ethical handling and preventing harm.
56	Intelligent Customer Support Automation	AILuminate (Privacy)	Preventing the leakage of sensitive personal information is critical in customer support, making this metric extremely relevant to assessing the system's ability to operate safely and avoid privacy-related harm.
3372	Legal Document Analysis	AILuminate (Privacy)	In legal document analysis, protecting sensitive personal information is crucial to prevent harm and comply with legal ethics, making this privacy safeguard metric highly relevant to the safety dimension of the system.
674	Automated Email Triage and Response	AILuminate (Privacy)	Because the automated email system handles potentially sensitive customer data, a metric that rigorously evaluates privacy safeguards is critically important for ensuring that harmful leaks do not occur, making it extremely relevant for the safety dimension.
3606	Litigation Prediction	AILuminate (Privacy)	Ensuring that the model does not divulge sensitive, nonpublic personal information is crucial in the legal field, making this privacy-protection assessment extremely relevant to the safe deployment of the system.
3474	Legal Reasoning Assistant	AILuminate (Privacy)	In a legal context where confidentiality is paramount, ensuring that the system does not inadvertently reveal private or nonpublic information is crucial, making this metric highly relevant from a safety standpoint.
2141	Tax Compliance Advisor	AILuminate (Privacy)	In the financial services context, particularly for a tax compliance advisor, preventing the exposure of sensitive financial or personal data is crucial, making this privacy metric highly relevant to safety despite not being developed specifically for tax tasks.
5228	Genomic Research Analysis	AILuminate (Privacy)	Since genomic research often involves sensitive genetic data that must be protected, a metric focused on preventing exposure of personal information is highly relevant for ensuring the model operates safely and ethically.
2710	Employee Performance Analytics	AILuminate (Privacy)	Given that employee performance data is inherently sensitive, a metric that rigorously assesses privacy safeguards is extremely relevant, ensuring the system can prevent exposure of nonpublic personal information.
2990	Automated Knowledge Base Maintenance	AILuminate (Privacy)	Since the metric directly evaluates the model’s ability to prevent leakage of sensitive, nonpublic personal information, it provides critical insight into a key safety aspect that is highly relevant to systems handling potentially sensitive data in a knowledge management context.
1933	Insurance Claims Fraud Detection	AILuminate (Privacy)	The metric directly assesses the model’s capability to prevent the exposure of sensitive, nonpublic personal data, which is crucial in insurance to protect policyholders and mitigate financial harm. This makes it highly relevant for ensuring safe deployment in an insurance fraud detection context.
5087	Smart Building Management	AILuminate (Privacy)	Since smart building management systems handle occupancy data and may expose personal information, a strong privacy safeguard is important; this metric directly tests the model’s ability to avoid disclosing sensitive personal data, making it highly relevant for the safety dimension.
5452	API Integration Assistant	AILuminate (Privacy)	The metric is highly relevant for ensuring the system avoids exposing sensitive information (such as API keys or user credentials), which is essential for a tool handling authentication and best practices in API consumption.
6102	Cybersecurity Threat Detection	AILuminate (Privacy)	Preventing the exposure of sensitive, nonpublic personal information is critical in a cybersecurity context, making this metric extremely relevant to ensuring that the system operates safely without causing additional security risks.
93	Customer Segmentation and Targeting	AILuminate (Privacy)	Since protecting sensitive personal data is critical in an advertising & marketing context, this privacy-focused metric is extremely relevant for ensuring the system does not inadvertently expose private information, thereby preventing potential harm.
731	Customer Feedback Analysis	AILuminate (Privacy)	Ensuring that sensitive customer information is not inadvertently exposed is critical in a feedback analysis system, making this metric extremely relevant for assessing the model’s adherence to privacy standards and ethical data handling.
5010	Property Valuation	AILuminate (Privacy)	Since handling personal and property-related data may involve sensitive information, using this metric to ensure that the system avoids exposing private details is highly relevant for maintaining safety and ethical standards.
1507	Curriculum Design	AILuminate (Privacy)	Since the metric evaluates how well the system protects sensitive personal information, it is highly relevant to ensuring that student, teacher, and administrator data remain secure and private in an educational context.
2465	Patient Risk Prediction	AILuminate (Privacy)	Since protecting sensitive health information is critical in healthcare, this metric is extremely relevant for safety—it directly measures the model’s ability to avoid exposing private, nonpublic patient data, reducing risks of harm and bias.
1734	Credit Risk Predictor	AILuminate (Privacy)	Given that a credit risk predictor handles sensitive financial and personal data, a metric evaluating the safeguarding of personal privacy is highly relevant to ensuring ethical handling of such data and preventing harmful exposures. While not designed specifically for this use case, strong privacy protections are a critical component of overall system safety.
6114	Personalized Product Recommendations	AILuminate (Privacy)	Since the use case handles sensitive customer behavior and purchase history, ensuring strong protection of personal information is critical; this metric directly assesses the system’s ability to prevent exposing private data and thus is extremely relevant to safety.
2504	Population Health Management	AILuminate (Privacy)	In healthcare, especially when handling sensitive patient data, strong privacy safeguards are critical; this metric directly gauges the model’s capacity to protect personal information, making it extremely relevant for the use case’s safety dimension.
2007	Insurance Claims Processing	AILuminate (Privacy)	Since insurance claims processing involves handling highly sensitive personal information, a robust privacy safeguard is critical; this metric directly evaluates that aspect, making it extremely relevant for ensuring safe operations.
3311	Contract Analysis	AILuminate (Privacy)	Ensuring the protection of sensitive personal data is crucial for legal applications. The metric is highly relevant, as a contract analysis system must reliably prevent disclosure of nonpublic or private data to avoid legal and ethical risks.
2099	Insurance Policy Pricing Optimization	AILuminate (Privacy)	Ensuring that sensitive personal and financial data is not exposed is critical in the financial services domain, making this privacy safeguard metric highly relevant to the safety aspects of the use case.
2927	Workforce Planning and Analytics	AILuminate (Privacy)	Since HR systems invariably handle sensitive personal data, a metric that examines how well a model prevents the exposure of private information is highly relevant for ensuring ethical and secure operations, making it quite applicable in this context.
4444	Content Recommendation Engine	AILuminate (Privacy)	Ensuring that the system does not expose sensitive personal data is highly relevant for any platform dealing with user information. Although not originally designed for content recommendation, robust privacy protection is critical in protecting users and upholding ethical data practices in this domain.
1593	Personalized Tutor	AILuminate (Privacy)	Given the personalized tutor will handle sensitive student data, robust privacy protection is critical, making this metric extremely relevant for ensuring the system operates safely without exposing private information.
955	Military Intelligence Analysis	AILuminate (Privacy)	In the context of military intelligence, preventing the disclosure of sensitive, nonpublic information is critical, making this metric extremely relevant for ensuring robust privacy protection and operational security.
2613	AI-Powered Recruitment	AILuminate (Privacy)	Since the recruitment system processes sensitive personal information, the ability to prevent disclosures of nonpublic personal data is critical. This metric directly measures safeguards against privacy breaches, making it extremely relevant to ensuring the system operates safely and ethically.
2887	Workflow Attrition Estimator	AILuminate (Privacy)	Because protecting nonpublic personal data is critical in the HR context, this metric is extremely relevant for ensuring that the system does not inadvertently expose sensitive employee information.
4883	Building Performance Analysis	AILuminate (Privacy)	Since building systems may handle occupant or facility data that could be sensitive, evaluating the model’s ability to prevent leakage of personal information is highly relevant for maintaining operational and ethical safety in this context.
2806	Resume Screener	AILuminate (Privacy)	Since the system handles candidate personal information, ensuring that private data is not inadvertently exposed is crucial; the metric is highly relevant in assessing the safety measures that protect individuals’ sensitive information, even if it isn’t tailored solely to HR contexts.
2242	Diagnostic Support System	AILuminate (Privacy)	In healthcare, protecting sensitive personal data is crucial, and this metric directly measures a system’s ability to guard against the exposure of private information—making it extremely relevant for ensuring patient safety and adherence to ethical standards.
4600	Clinical Trial Optimization	AILuminate (Privacy)	Since clinical trial optimization involves handling highly sensitive patient data, a metric that rigorously tests the system’s ability to prevent unauthorized disclosure of private information is critically important and directly aligned with ensuring the safety and ethical handling of data.
821	Virtual Customer Service Agent	AILuminate (Privacy)	This metric directly assesses the model’s ability to prevent exposing sensitive personal information, a critical safety requirement in customer service settings. Its focus on protecting privacy makes it extremely relevant for ensuring the agent operates without causing harm.
2391	Medical Image Analysis	AILuminate (Privacy)	In a healthcare setting, safeguarding patient data is critical; this metric directly measures the model's capability to prevent exposure of sensitive information, making it extremely relevant to ensuring safe operation.
851	Virtual Customer Service Agent	AILuminate (Overall)	The metric directly evaluates the safety of AI text interactions across various hazard categories, which is highly relevant for ensuring that a virtual customer service agent operates without causing harm or bias.
3477	Legal Reasoning Assistant	AILuminate (Overall)	Since the metric comprehensively assesses safety across a wide range of hazard categories, it is highly relevant for ensuring that a Legal Reasoning Assistant minimizes harmful outputs and adheres to ethical guidelines, even though its design is for general-purpose AI systems.
3229	Knowledge Discovery and Mining	AILuminate (Overall)	The metric robustly evaluates safety across multiple hazard categories and aligns well with ensuring that a knowledge management tool avoids harmful or biased outputs, making it highly relevant to the safety dimension of the use case.
1539	Curriculum Design	AILuminate (Overall)	Since the metric provides a comprehensive evaluation of safety across multiple hazard categories in text interactions, it is highly relevant to ensuring that the curriculum design AI avoids harmful outputs and ethical pitfalls, even though it is not tailored specifically for educational content.
4325	Audience Analytics and Insights	AILuminate (Overall)	Since the metric comprehensively evaluates safety across multiple hazard categories for text-based outputs, it is highly relevant for ensuring that any interactive or written communication (if present) in the system remains ethical, bias‐free, and non-harmful.
778	Customer Feedback Analysis	AILuminate (Overall)	AILuminate offers a comprehensive evaluation of safety across multiple hazard categories, which is directly relevant for ensuring that outputs (such as responses or insights) do not introduce harmful biases or issues. Its relative safety grading helps ensure that the AI system operates ethically and avoids generating problematic content in customer service contexts.
3088	Expertise Location System	AILuminate (Overall)	AILuminate provides a comprehensive evaluation of safety across multiple hazard categories, which is highly relevant for ensuring that text interactions within the system do not produce harmful, biased, or inappropriate content. This is critical for maintaining ethical guidelines and user trust in a system that connects employees and manages sensitive internal communications.
37	Intelligent Customer Support Automation	AILuminate (Overall)	Since the metric offers a detailed and comprehensive evaluation of the AI's safety features across multiple hazard categories, it is extremely relevant for ensuring that the chatbot minimizes harms and adheres to ethical standards.
683	Automated Email Triage and Response	AILuminate (Overall)	Since the use case involves the AI automatically generating and sending responses to customers and stakeholders, ensuring safe, unbiased, and non-harmful communication is essential. AILuminate provides a comprehensive hazard assessment for text outputs, making it highly relevant for evaluating the overall safety of the system’s responses.
6027	Content Moderation	AILuminate (Overall)	Given that AILuminate comprehensively grades safety across a wide range of hazard categories, it provides extremely relevant insight into the system’s ability to avoid harmful outputs—a critical aspect for content moderation systems focused on reducing toxic content.
3506	Legal Research Assistant	AILuminate (Overall)	Since it comprehensively evaluates safety across multiple hazard categories with a rigorous benchmark, it is highly relevant for ensuring the legal research assistant avoids harmful biases and unethical outputs, even though it is aimed at general-purpose models.
2167	Tax Compliance Advisor	AILuminate (Overall)	The metric is designed to broadly assess potential harms across many hazard categories in AI outputs, making it highly relevant for ensuring that the Tax Compliance Advisor operates without generating harmful, biased, or ethically problematic content.
1597	Personalized Tutor	AILuminate (Overall)	Since the tool comprehensively assesses text-based safety across multiple hazard categories, it is highly relevant for ensuring that a student-facing tutoring system maintains ethical, bias-free, and harm-averse interactions.
1411	Design Trend Analysis	AILuminate (Overall)	Since the metric comprehensively evaluates safe text generation across varied hazards, it is highly relevant for ensuring that the system avoids harmful or biased outputs, even though it is designed for general-purpose chat models.
4524	Real-time Content Moderation	AILuminate (Overall)	Given that the use case centers on reducing toxic and harmful content, the metric’s comprehensive assessment of safety across multiple hazard categories is extremely relevant. It directly gauges the robustness of safety mechanisms—critical for responsible real-time content moderation.
6031	Content Moderation	AILuminate (Non-violent Crimes)	By evaluating the prevention of content that could enable non-violent crimes, the metric directly relates to key safety concerns—ensuring the model doesn't propagate harmful instructions or disallowed behavior—even though it only addresses one aspect of overall safety.
2174	Tax Compliance Advisor	AILuminate (Non-violent Crimes)	Since tax compliance systems must strictly avoid providing advice that could facilitate fraudulent or criminal activities, a metric that evaluates safeguards against non-violent crimes (including fraud) is highly relevant to ensuring safe and compliant behavior.
2017	Insurance Claims Processing	AILuminate (Non-violent Crimes)	Since insurance claims processing involves risks surrounding fraud and unethical practices, a metric that gauges the AI’s ability to avoid endorsing non-violent criminal behavior is highly pertinent. The metric’s focus on preventing fraudulent or criminal content aligns well with the safety requirements of maintaining ethical standards within the financial services environment.
3217	Knowledge Discovery and Mining	AILuminate (Intellectual Property)	The metric is highly relevant for safety in this context, as preventing the unauthorized disclosure or misuse of intellectual property is critical when handling sensitive organizational documents and communications. It provides a strong proxy for safeguarding against IP-related risks, an important aspect of safe deployment.
1250	Automated Design Generation	AILuminate (Intellectual Property)	Protecting against intellectual property infringement is essential in design and creative services, as inadvertent copying or replication of existing works can lead to legal issues. This metric is highly relevant for ensuring the AI system operates responsibly in terms of IP, even though it was originally benchmarked for general-purpose systems.
4657	Drug Discovery Acceleration	AILuminate (Intellectual Property)	Effective protection of intellectual property is critical in pharmaceutical research given the high stakes of proprietary data, so measuring IP-compliance is highly relevant to the safety aspect of the system.
5423	API Integration Assistant	AILuminate (Intellectual Property)	The metric is highly relevant to the safety dimension because it ensures the system avoids generating responses that breach intellectual property rights, which is critical to prevent legal risks and maintain ethical standards in code generation and API consumption.
1165	Asset Management and Organization	AILuminate (Intellectual Property)	Given the creative nature of the assets involved, strong protection against intellectual property infringement is vital, making this metric highly relevant for evaluating the system’s ability to adhere to safety and ethical guidelines.
5666	Code Documentation Generator	AILuminate (Intellectual Property)	Given that a documentation generator might inadvertently reveal or incorporate copyrighted content, having robust IP protection is highly important. The metric’s focus on preventing copyright infringement is therefore highly relevant to the overall safe operation of the system, though it is tailored more toward legal compliance than a complete safety profile.
4375	Automated Video Editing	AILuminate (Intellectual Property)	Since media and entertainment work is highly sensitive to issues involving copyright and intellectual property, a metric focused on preventing IP violations is critically important for ensuring that the system operates safely and ethically.
5709	Code Generation Assistant	AILuminate (Intellectual Property)	Since the code generation assistant must ensure that no copyrighted or proprietary code is inadvertently reproduced, this metric is highly relevant for evaluating and ensuring safe operation in terms of IP compliance.
40	Intelligent Customer Support Automation	AILuminate (Intellectual Property)	Since preventing unauthorized use of copyrighted material is a crucial legal and ethical safeguard, this metric is highly relevant for ensuring the chatbot operates safely and avoids potential legal issues. However, it targets one specific safety aspect and does not cover all possible risks in customer support interactions.
1360	Design Trend Analysis	AILuminate (Intellectual Property)	Strong protection against intellectual property violations is crucial in the design and creative sector to avoid legal and ethical issues, making this metric highly relevant for ensuring safe, compliant operations in the use case.
884	Autonomous Defense Systems	AILuminate (Indiscriminate Weapons)	The metric directly measures safeguards against enabling harmful weapon creation and thus is critically aligned with ensuring that the system avoids exacerbating dangerous scenarios, a paramount concern in military safety contexts.
962	Military Intelligence Analysis	AILuminate (Indiscriminate Weapons)	Ensuring that the AI does not provide information that could enable the creation of indiscriminate weapons is highly relevant to defense applications, making this metric a critical safety test even though its focus is on a specific type of harmful response.
1096	Military Training Simulation	AILuminate (Indiscriminate Weapons)	The metric is extremely relevant for safety, as it directly tests the model's capability to avoid facilitating harmful content, a critical aspect in defense contexts where ethical handling of sensitive information is paramount.
2012	Insurance Claims Processing	AILuminate (Hate)	Since the metric evaluates safeguards against hateful or discriminatory outputs, it is highly relevant to the safety dimension—helping ensure that communications with customers and internal stakeholders remain respectful and unbiased in a high-stakes financial environment.
805	Virtual Customer Service Agent	AILuminate (Hate)	Preventing demeaning or discriminatory content is critical in customer interactions, making this metric extremely relevant for ensuring ethical, respectful, and unbiased communication in a virtual customer service context.
34	Intelligent Customer Support Automation	AILuminate (Hate)	This metric is highly critical for ensuring the system does not produce harmful or discriminatory content, which is essential for maintaining ethical interactions with customers in a support setting.
2580	AI-Powered Recruitment	AILuminate (Hate)	Preventing hateful or discriminatory content is critical in a recruitment tool, as such harms can contribute to bias and unethical hiring practices. This metric directly evaluates an important safety aspect—ensuring the AI operates without causing discriminatory harm—making it extremely relevant.
1581	Personalized Tutor	AILuminate (Hate)	Ensuring that the model does not produce hateful or dehumanizing content is critically important in an educational context, as it protects students and other stakeholders from harm and upholds ethical guidelines.
2801	Resume Screener	AILuminate (Hate)	Since resume screening systems must avoid discriminatory or biased decisions, a metric that assesses a model’s ability to prevent hateful content is highly relevant for ensuring that safety and fairness guidelines are met in HR applications.
350	Social Media Campaign Analysis	AILuminate (Hate)	Since social media campaigns can have significant public impact, ensuring that the model avoids hate speech and discriminatory content is very important, making this metric highly relevant for maintaining ethical standards and protecting brand reputation.
2541	Population Health Management	AILuminate (Hate)	In a healthcare context—where output might impact vulnerable populations and ethical communication is critical—a metric that tests for hateful or dehumanizing content is highly relevant for ensuring safe and unbiased interactions.
6023	Content Moderation	AILuminate (Hate)	The metric is extremely relevant to safety since it directly evaluates the system’s proficiency at preventing responses that demean or dehumanize people, addressing critical concerns around bias and discriminatory content that are central to content moderation use cases.
730	Customer Feedback Analysis	AILuminate (Hate)	Since the metric is designed to evaluate and mitigate hateful or discriminatory responses, it is highly relevant for ensuring that outputs do not perpetuate harmful biases in customer communications, which is critical when dealing with sensitive feedback and ensuring ethical customer support practices.
92	Customer Segmentation and Targeting	AILuminate (Hate)	Since the use case involves delivering personalized marketing messages where bias could harm customer trust and affect ethical standards, ensuring the system avoids discriminatory or hateful outputs is critically important.
4493	Real-time Content Moderation	AILuminate (Hate)	The metric is directly geared toward assessing and preventing hate speech and discriminatory responses, making it extremely relevant for ensuring that the AI system adheres to robust ethical guidelines and maintains a safe user environment in live content contexts.
2257	Diagnostic Support System	AILuminate (Hate)	Preventing demeaning or discriminatory responses is crucial in healthcare where biased output could harm vulnerable patients, making this metric highly relevant for assessing the safe operation of the diagnostic support system.
1467	Automated Assessment & Grading	AILuminate (Hate)	Since the educational use case must avoid generating demeaning or discriminatory content, a metric that evaluates safeguards against hate speech is highly relevant for ensuring ethical and safe interactions.
3599	Litigation Prediction	AILuminate (Hate)	Although not tailored for legal systems, the ability to avoid discriminatory or dehumanizing content is highly relevant to ethical operations in legal contexts, ensuring that outputs adhere to fairness and safety standards.
4416	Content Recommendation Engine	AILuminate (Hate)	This metric is extremely relevant since it directly evaluates the system’s safeguards against generating harmful or discriminatory content—a critical safety and ethical concern in any content recommendation setting.
2744	Employee Performance Analytics	AILuminate (Hate)	Preventing hateful or discriminatory content is critical in an HR setting, and this metric directly evaluates the model’s ability to safeguard against harmful outputs that could adversely affect employees, making it extremely relevant for ensuring ethical and unbiased communication.
2660	Employee Engagement Analysis	AILuminate (Hate)	Since the use case impacts employee well‐being and workplace culture, ensuring that the model minimizes discriminatory or hateful content is highly relevant. This metric provides substantial insights into the safety guardrails critical in maintaining an ethical HR environment.
1247	Automated Design Generation	AILuminate (Hate)	Since ensuring that automated design outputs do not inadvertently include demeaning or hateful content is important for ethical and reputational reasons, this metric is highly relevant for gauging whether the system adheres to safety guidelines, even though its design domain is not primarily focused on hate prevention.
679	Automated Email Triage and Response	AILuminate (Hate)	Since the use case involves automated responses that could potentially impact external customers and other stakeholders, ensuring that the system does not produce discriminatory or hateful content is critically important.
2168	Tax Compliance Advisor	AILuminate (Hate)	Ensuring that AI outputs do not demean or dehumanize individuals is important, especially in high-stakes financial advice; although the metric is generic to hateful content, it is highly relevant for maintaining ethical and non-discriminatory behavior in the system.
3435	Legal Reasoning Assistant	AILuminate (Hate)	Since the legal domain demands high ethical standards and non-discriminatory communication, measuring hate prevention is highly relevant to ensuring the system avoids harmful, biased outputs.
3429	Legal Reasoning Assistant	AILuminate (Defamation)	Preventing defamation is crucial in the legal context to avoid reputational harm and ethical breaches; thus, this metric is highly relevant for ensuring that the system operates safely in high-stakes legal environments.
2570	AI-Powered Recruitment	AILuminate (Defamation)	Since the metric evaluates the ability to avoid spreading verifiably false and potentially harmful statements about individuals, it is highly relevant for ensuring that the recruitment AI does not inadvertently damage candidates’ reputations or contribute to harmful bias.
6005	Content Moderation	AILuminate (Defamation)	Since the metric directly tests the system's ability to avoid spreading misinformation that can harm personal reputations, it is extremely relevant for ensuring that the system operates safely and ethically in content moderation scenarios.
3112	Expertise Location System	AILuminate (Defamation)	Since the system deals with personal and professional reputations, ensuring that outputs do not spread harmful misinformation is critical, making a defamation safeguard metric highly relevant to its safe operation.
64	Intelligent Customer Support Automation	AILuminate (Defamation)	Preventing the spread of harmful misinformation is critical, and this metric directly tests the system’s ability to avoid generating potentially injurious false statements, which is essential for ensuring ethical and safe interactions in any customer support context.
4533	Real-time Content Moderation	AILuminate (Defamation)	Since the metric directly assesses the system’s ability to avoid generating harmful misinformation that could defame individuals, it is extremely relevant from a safety perspective, offering critical information about the model’s adherence to ethical guidelines and harm prevention.
3584	Litigation Prediction	AILuminate (Defamation)	In a legal context, ensuring that outputs do not spread harmful misinformation is crucial, as defamatory statements can have serious legal and ethical consequences; hence, this metric is extremely relevant to safeguarding against reputation harm.
2743	Employee Performance Analytics	AILuminate (Defamation)	Ensuring that the system does not produce defamatory or misleading statements is critical in HR applications, where inaccurate outputs could harm employee reputations and trigger significant ethical and legal issues.
3511	Legal Research Assistant	AILuminate (Defamation)	Ensuring that the system does not produce defamatory or factually harmful statements is highly important in the legal context, making this metric highly relevant for safeguarding against potential reputational harm even though it doesn’t cover all legal-specific safety concerns.
850	Virtual Customer Service Agent	AILuminate (Defamation)	Since the metric is designed to assess safeguards against disseminating false and harmful statements about individuals, it is highly relevant to ensuring that a virtual customer service agent operates ethically and minimizes risks associated with reputational harm.
4510	Real-time Content Moderation	AILuminate (Child Sexual Exploitation)	The metric is directly aimed at preventing a particularly harmful and ethically critical issue, making it extremely relevant for ensuring that the system operates without causing severe harm; its focus on child sexual exploitation aligns tightly with high-priority safety and ethical requirements in content moderation.
6009	Content Moderation	AILuminate (Child Sexual Exploitation)	Because it focuses on safeguarding against one of the most egregious harmful content types—child sexual exploitation—the metric is extremely relevant and critical for ensuring the system does not cause severe harm or violate ethical guidelines in the context of content moderation.
1624	Personalized Tutor	AILuminate (Child Sexual Exploitation)	Given that the use case involves students (often minors), robust protection against child abuse content is critical, making this metric extremely relevant for ensuring that the tutoring system operates safely and ethically.
4435	Content Recommendation Engine	AILuminate (Child Sexual Exploitation)	Although narrowly focused on preventing child sexual exploitation content, it is highly relevant to ensuring that the engine avoids severe harm by filtering out dangerous content, a critical aspect of user safety in media platforms.

4 Context-adapted Model Scoring

Once we’ve filtered models based on non-negotiable requirements, we enter more nuanced territory. Models that meet an organization’s baseline requirements must then be evaluated across multiple dimensions including safety, capability, and cost. This is where Model Trust Scores provide their most sophisticated insights, helping organizations navigate complex tradeoffs in a systematic way.

4.1 Methodology

How exactly do we measure and compare these different dimensions? Our methodology combines multiple data sources and a novel benchmark synthesis approach to create a context-adapted scoring engine.

One can think of the Model Trust Scores as “projecting” the capabilities and safety of a model onto a set of use cases, which gives a more context-aware evaluation than simple benchmark comparisons. We also have the ability to synthesize the capabilities in a use-case agnostic way, which ends up ranking models based on their generic properties.

4.1.1 Data Sources

1. Model Benchmarks We aggregated 60+ benchmarks from multiple sources including provider’s own reporting of benchmark performance, LiveBench, ScaleAI’s evaluations, MLCommon’s AILuminate, vals.ai, Artificial Analysis, Math Arena, Simplebench, and Huggingface Leaderboard.

   A non-exhaustive list of the benchmarks we synthesized are included below:

   • General Capability Benchmarks:
     • Knowledge & Reasoning: MMLU, GPQA-Diamond, DROP, FRAMES
     • Math & Science: MATH-500, AIME 2024, AIME 2025, CNMO 2024, LiveBench (Math), LiveBench (Data Analysis)
     • Language Understanding: AlpacaEval2.0, IF-Eval, SimpleQA, LiveBench (Language), LiveBench (Instruction Following)
     • Chinese Language: CLUEWSC, C-Eval, C-SimpleQA
   • Domain-Specific Benchmarks:
     • Programming & Software: Codeforces Ratings, SWE Verified, Aider-Polyglot, LiveCodeBench, LiveBench, Chatbot Arena Coding
     • Legal: LegalBench, CaseLaw, ContractLaw, TaxEval
     • Finance: CorpFin, FailSafeQA (Context Grounding, Robustness, Compliance)
     • Medical: MedQA
   • Safety Benchmarks:
     • MLCommons’ AILuminate suite evaluating 12 hazard categories including:
       • Content Safety: Child Sexual Exploitation, Sexual Content, Hate Speech
       • Criminal Activity: Non-violent Crimes, Sex-Related Crimes
       • Harmful Advice: Specialized Advice, Suicide & Self-Harm
       • Other Risks: Defamation, Privacy, Intellectual Property, Indiscriminate Weapons
     • AIR-Bench from Stanford, which measures refusal rate for a number of different risk categories. Their risk categories are hierarchically organized into 4 tiers. Below you can see the tier 1 and 2 risks. We include refusal risks at the “level 3” level, which gives us 45 separate refusal categories.
       • System and Operational Risks: (Security Risks & Operational Misuses)
       • Content Safety: (Violence & Extremism, Hate/Toxicity, Sexual Content, Child Harm, Self-harm)
       • Societal Risk: (Political Usage, Economic Harm, Deception, Manipulation, Defamation)
       • Legal and Rights Related Risks: (Fundamental Rights, Discrimination/Bias, Privacy)
   • Operational Metrics:
     • Cost: Blended Price (USD/1M Tokens)
     • Speed: Median Tokens/s

   Model Scores

   For some models there are multiple versions and deployment contexts. For instance, there are multiple versions of Llama 3-70B, tuned for latency, cost, instruction following, etc. For others the “model” most benchmarks are evaluated on is actually an AI system, composed of multiple models accessed via API. OpenAI’s API is an example of this. Further complicating matters, different providers may put more safeguards in the model itself, while others may put more safeguards in the API. For instance, Mistral has a moderation API that significantly improves the safety of the AI system.

   We do not intend for this proof of concept to be comprehensive for all possible models and model variants. Whenever possible, we report the behavior of the model itself without additional safeguards, and choose evaluation results we believe are representative of the model’s general performance across deployment scenarios.

   Benchmark Coverage Limitations

   Benchmarks do not have even coverage over all models. Certain metrics are almost ubiquitous while others are rarely used. The third party ecosystem of AI evaluations and leaderboards is growing, but still is maturing. This results in benchmark-specific leaderboards that are not as responsive as we would like. For instance, in the last couple of months a number of new models have been released - o3, DeepSeek R1, Claude-3.7, and Grok 3. There is uneven coverage of these models by different measures.

   This is a particular issue with safety benchmarks, which are under invested in by the ecosystem as a whole. As an example, MLCommon’s AILuminate is one of the most comprehensive third-party safety evaluation of AI models that is inclusive of both open weight and propriety models, but has not been updated for the most recent models. This leaves a gap (Disclosure: Credo AI is a member of MLCommons and supported the creation of AILuminate). AIR-Bench has been updated more recently and thus supports safety claims on more recent models.

   We will continuously incorporate new benchmarks and updated scores into the Model Trust Scores as they are available.

   Below you can see the number of benchmarks we have for each model, separated by capability and safety dimensions.

	# capability benchmarks	# safety benchmarks	# total benchmarks
GPT-4o-0513	53	60	113
Claude-3.5-Sonnet-1022	50	59	109
Gemini-1.5-Pro	36	59	95
DeepSeek-R1	45	49	94
OpenAI-O1-1217	42	48	90
Claude-3.7-Sonnet	38	47	85
Llama-3.1-405B	26	58	84
OpenAI-O3-mini-high	35	47	82
OpenAI-O1-mini	36	46	82
DeepSeek-V3	37	45	82
OpenAI-O3-mini-medium	32	48	80
Claude-3.7-Sonnet-Thinking	33	47	80
Gemini-2.5-Pro-0325	32	46	78
Mistral-Large-2	15	57	72
OpenAI-O3-high	26	46	72
Llama-4-Maverick-17B	25	46	71
GPT-4.1	23	46	69
Gemini-2.0-Flash-Thinking-0121	22	45	67
Grok-3-Beta	21	44	65
Gemini-2.0-Pro-0121	18	45	63
Grok-3-Mini-Beta	18	44	62
GPT-4.5	17	45	62
Cohere-Command-R-Plus	17	44	61
OpenAI-O3-medium	11	45	56
Grok-3-Think	6	43	49
Llama-3.3-70B	26	4	30
DeepSeek-V3-0324	23	1	24
Cohere-Command-A	20	1	21
Mistral-Large	16	1	17

2. Industry Use Cases
   • 95 representative use cases across 21 industries
   • Each use case has a description, proposed benefits, impacted people, and risk scenarios drawn from Credo AI’s risk library.

The Use Cases we used in our analysis are representative of the kinds of use cases that are prevalent in the enterprise world, but they are only meant to be illustrative. They are neither exhaustive nor at the level of detail that an individual enterprise would ideally use within the context of their organization and business. However, we believe that the use cases we aggregated can serve as a reasonable starting point to showcase the Model Trust Score Framework’s abilities and give ecosystem level insights that can be refined over time.

You can see the breakdown of use cases per industry below.

	index	Industry	Number of Use Cases
0	17	Software Development	8
1	6	Financial Services	7
2	8	Human Resources	6
3	10	Legal	5
4	12	Manufacturing	5
5	7	Healthcare	5
6	19	Transportation	4
7	16	Sciences	4
8	15	Real Estate & Construction	4
9	14	Pharmaceutical	4
10	13	Media & Entertainment	4
11	0	Advertising & Marketing	4
12	11	Logistics	4
13	1	Agriculture	4
14	9	Knowledge Management	4
15	5	Education	4
16	4	Design & Creative Services	4
17	3	Defense	4
18	2	Customer Service & Support	4
19	20	Utilities	4
20	18	Technology	3

4.1.2 Analysis Framework

We combine these data sources through a multi-step process:

1. Benchmark Aggregation: Normalize and combine various benchmarks, accounting for varying scales and methodologies. We used a similar normalization process as huggingface.

2. Generic Model Scoring: Each model is scored without use case context to get a baseline understanding of their capabilities and safety. For this synthesis, we averaged normalized evaluations within their respective categories (“capability” or “safety”) to arrive at a raw score per category that is between 0 and 1.

   We use this raw score to update a conservative baseline assumption (a “prior”) that any model’s capability and safety levels are relatively low (specifically, 0.3). When we observe actual performance data from benchmarks, we adjust our assessment away from this conservative starting point based on the “evidence strength” (a function of how many evaluations we have). This conservative approach reflects the precautionary principle and accounts for reporting bias, as providers typically publish favorable results while withholding poor ones.

   We also bring operational metrics into the overall picture, sourced from Artificial Analysis. Cost is a function of the number of tokens processed, and speed is a function of the number of tokens processed and the speed of the model. We cost into affordability scores by the following formulas:

   $affordability=1-\frac{cost}{max\mathrm{\_}cost}$

   Finally, we combine the four dimensions into a single “overall score” for each model. We use a weighted geometric mean to combine the scores. The weighted geometric mean has a few useful properties:

   1. Zero Preservation: If either safety or capability is 0, the final score will be 0. This makes sense because a model that is either completely unsafe (safety_score = 0) or completely incapable (capability_score = 0) should be considered unsuitable for the use case, regardless of its other score.

   2. Penalizes Imbalance: Unlike arithmetic mean, geometric mean penalizes large disparities between the values. For example: Two scores of (0.5, 0.5) give the same result as arithmetic mean: 0.5 But scores of (0.1, 0.9) will give a lower geometric mean (~0.3) than arithmetic mean (0.5) This is desirable because we generally want models that are both safe AND capable, not just high in one dimension.

   In this report we evenly weight all 4 dimensions, but operational use of this approach can adjust these weights based on the user’s goals.

   The final generic scoring results in:

   • Overall Score
   • Capability
   • Safety
   • Operational metrics (affordability/speed)

3. Relevance Scoring (Use Case Mapping): For each industry use case, we determine the relevance of each benchmark separately for capability and safety dimensions using a novel relevance scoring system. This is the key step that allows us to compare models across different use cases. It determines how benchmark information is “projected” onto the use case.

   This system evaluates benchmarks on a 5-point scale for both capability and safety dimensions independently:

   • 5 (Extreme): Provides critical information. The metric context is near identical to the use case.
   • 4 (High): Metric context clearly generalizes to the use case.
   • 3 (Moderate): Metrics related to use case but rather generic.
   • 2 (Low): Provides only general insights
   • 1 (None): Offers no meaningful signal

   While this scale is ordinal, we transform the values to reflect that highly relevant benchmarks are significantly more valuable than low relevance ones. This matches real-world AI development where generic benchmarks provide initial signals, but specific evaluations become increasingly important. We use the following transformation:

   $relevance\mathrm{\_}weight=((relevance\mathrm{\_}score-1)/4{)}^2$

   This transformation squares the normalized score, making the difference between high and low relevance more pronounced. For example, a score of 5 becomes 1.0, a score of 4 becomes 0.56, a score of 3 becomes 0.25, a score of 2 becomes 0.06, and a score of 1 becomes 0.

4. Context-adapted Model Scoring: Each model is scored within the context of a specific use case. We follow the same statistical approach as the generic evaluation - combining metrics within categories and using our conservative prior - but now each metric’s contribution is weighted by its relevance score for that specific dimension (capability or safety) and that use case. This means highly relevant benchmarks have much more influence on the final score than benchmarks with low relevance. The strength of evidence (how much we move away from our prior) now depends not just on how many evaluations we have, but how relevant those metrics are to the specific use case. A few highly relevant benchmarks can provide stronger evidence than many low-relevance ones. These context-adapted scores, which we call “Model Trust Scores”, provide a more nuanced view of model performance in specific enterprise contexts.

5. Aggregation and Analysis: With scores for each model and each use case we can explore the full spectrum of model X use-case capacities. We also aggregate the scores such that we have model-level, industry-level and model X industry scoring.

This methodology enables us to move beyond simple benchmark comparisons to provide context-aware recommendations that consider the full spectrum of enterprise requirements.

4.2 Results

4.2.1 Generic Model Scoring

We first created use-case agnostic scores for each model along the 4 dimensions: capability, safety, cost and latency, and calculated the overall score. T The below interactive plot shows the results for each dimension.

We can also look at how dimensions relate to each other in a multi-dimensional space. For instance, capability vs safety:

Similarly we can look at capability vs. affordability, perhaps the most important tradeoff for enterprise use cases. Note that o1 and GPT 4.5 are currently much more expensive than the rest of the models, so we restrict the x-axis to better see the tradeoffs amongst models with comparable costs.

The results broadly align with existing leaderboard rankings, which validates our methodology while highlighting important distinctions. For instance, reasoning models dominate general capabilities and DeepSeek R1 is exceedingly capable given its low cost. Grok 3 Beta (Think) is also impressively capable. However, both models don’t perform well on the safety tests.

Reasoning models also excel in safety. Only one non-reasoning model performs higher - Claude 3.5. However, rather than due to intrinsic properties, this result is likely due to Claude 3.5’s high performance on a greater number of public safety benchmarks than the other reasoning models. Specifically, AILuminate has not been updated for a number of months as of this publication and has not rated many of the newer reasoning models. Our observation that reasoning models excel here is not a surprise. OpenAI has shown that reasoning models can improve safety through deliberative alignment, and it would be in line with the previous trajectory of improved capabilities leading to better rule following and alignment (as long as the AI developer prioritizes safety in their training).

If we color by release date we can also see the general pushing out of the “pareto frontier” on any tradeoff line. That is, models are getting more capable AND safer AND affordable. Some model developers may choose a particular tradeoff (prioritizing capability over safety), but the models developed today are certainly improved generally vs the models from last year.

4.2.2 Relevance Scores

At the heart of Model Trust Scores lies our approach to calculating “relevance scores” - a systematic way to determine how applicable different AI benchmarks are to specific industry use cases. While our generic synthesis treated all benchmarks as equally important, we know this isn’t true in practice. A benchmark that’s crucial for evaluating content moderation capabilities might be irrelevant for financial analysis, and vice versa. The relevance scoring system addresses this challenge head-on. We addressed the general methodology above, but let’s get more concrete with an example.

4.2.2.1 Example: Content Moderation

To demonstrate how generic capabilities translate to real-world applications, let’s examine content moderation - a common enterprise use case with clear safety and capability requirements. This example illustrates how our relevance scoring system bridges the gap between abstract benchmarks and practical needs.

We use the “Content Moderation” information we created as context and applied the relevance rubric to each capability and safety benchmark to determine its relevance to the use case. One way of thinking about this visually is asking the question “Do the behaviors measured by this benchmark point in the same direction as the use case?”. The further away the benchmark points from the use case, the lower the relevance, with an orthogonal benchmark (90 degrees away) having no information about the use case.

The plot below builds on this metaphor and shows 5 example metrics taken from the full list along with explanations of why they got the score.

The relevance for all the metrics are shown below for the Content Moderation use case, ordered by relevance score.

	use_case	metric_name	capability_relevance	safety_relevance	capability_explanation	safety_explanation	industry
10838	Content Moderation	AIR-Bench-CelebratingSuffering	4.0	5.0	This metric is highly relevant because it directly assesses the system’s ability to detect and react appropriately to content that glorifies violence or minimizes suffering—an important facet of automated content moderation—even though it covers a specific type of challenging content rather than the entire moderation spectrum.	The metric is extremely relevant from a safety perspective since it evaluates the model’s refusal to endorse or propagate harmful narratives, ensuring that the system robustly prevents the normalization of violence and extremism, which is critical in protecting community well-being.	Technology
10842	Content Moderation	AIR-Bench-HateSpeech	4.0	5.0	The metric directly assesses the model’s ability to detect and refuse hate speech, which is a key function in automated content moderation; however, while highly relevant, it focuses primarily on hate speech and may not cover all aspects of content moderation tasks.	Given that managing hate speech is critical to ensuring a safe and non-toxic online environment, this metric is extremely relevant as it measures the model’s ability to avoid producing or facilitating harmful content, directly addressing ethical and bias-related safety concerns.	Technology
10849	Content Moderation	AIR-Bench-PerpetuatingHarmfulBeliefs	4.0	5.0	This metric directly tests the system’s ability to identify and handle hate and toxicity, which is a core requirement for effectively moderating user-generated content, even though its focus on “refusal” rather than explicit flagging may not cover every moderation nuance.	The metric is specifically designed to assess the avoidance of perpetuating harmful stereotypes and discriminatory ideologies, making it critically relevant for ensuring that content moderation does not inadvertently contribute to bias or harm.	Technology
10857	Content Moderation	AIR-Bench-SupportingMaliciousOrganizedGroups	4.0	5.0	This metric directly tests how well the system can identify extremist content and refuse to provide potentially dangerous content, which relates to the core task of moderating user-generated content effectively.	Since the metric gauges the refusal of assistance for violent extremist and criminal group support, it is critical in ensuring the system does not inadvertently promote harmful behavior, making it extremely relevant from a safety perspective.	Technology
10863	Content Moderation	AIR-Bench-Fraud	4.0	4.0	Although AIR-Bench-Fraud focuses on detecting and refusing deceptive or fraudulent content—which is only one slice of the broader content moderation task—it directly assesses abilities (e.g., identifying spam, scams, phishing) that are central to moderating user submissions in technology platforms.	By evaluating the model’s refusal behavior on fraudulent activities, the metric offers strong insights into its safety performance—specifically, its capacity to avoid facilitating harmful or deceptive practices—making it highly relevant to ensuring ethical and harm-averse content moderation.	Technology
5973	Content Moderation	AlpacaEval2.0 (LC-winrate)	3.0	1.0	The metric evaluates general language comprehension and comparative performance, which can partially indicate the model's ability to understand nuanced user content; however, it is not specifically designed for the nuanced requirements of content moderation.	This metric does not address critical safety issues such as bias, ethical adherence, or the avoidance of harmful outputs, making it largely irrelevant for assessing the safety aspects of content moderation tasks.	Technology
5980	Content Moderation	CLUEWSC (EM)	3.0	1.0	While the Chinese commonsense reasoning evaluated by CLUEWSC (EM) can indirectly reflect a model’s ability to comprehend nuanced language—potentially aiding in ambiguous content decisions—the test is not specifically tailored to the content moderation task.	The metric does not assess aspects related to bias avoidance, ethical behavior, or harm prevention, making it largely irrelevant for evaluating the safety dimensions crucial to content moderation.	Technology
5981	Content Moderation	LiveBench (Language)	3.0	1.0	LiveBench tests general language understanding and manipulation, which is foundational for comprehending user-generated content; however, its specific tasks (word puzzles, typo removal, synopsis unscrambling) do not directly assess domain-specific challenges like detecting toxicity or nuanced context in content moderation.	The metric does not provide any assessment related to safety aspects such as bias detection, ethical decision-making, or the prevention of harm, making it virtually irrelevant for evaluating safe operations in the content moderation context.	Technology
5987	Content Moderation	LiveBench (Average)	3.0	1.0	Although LiveBench (Average) assesses general reasoning, language, and problem-solving skills—which can indirectly support the technical aspects of content moderation—it does not specifically target the nuanced, context-sensitive judgment required for moderating user content.	LiveBench (Average) does not evaluate safety-related aspects such as bias mitigation, ethical decision-making, or harm reduction, making it virtually irrelevant for assessing the safety dimension in content moderation.	Technology
5991	Content Moderation	IF-Eval (Prompt Strict)	3.0	2.0	The metric focuses on strict adherence to instructions, which is somewhat generalizable to content moderation—ensuring that the system follows detailed moderation guidelines. It provides a moderately relevant signal for how well a model might execute specialized procedures in the domain.	While strict instruction following can help mitigate certain unsafe behaviors, this metric does not directly evaluate bias management or harm avoidance in content moderation decisions, making it only minimally relevant from a safety perspective.	Technology
5997	Content Moderation	ArenaHard (GPT-4-1106)	3.0	1.0	While ArenaHard is designed to test advanced reasoning with challenging queries, which can indirectly indicate robustness under complex scenarios, its focus is not tailored to the nuances of content moderation. It provides moderate insight into the model’s ability to handle diverse and unforeseen queries that may arise in content moderation.	ArenaHard does not specifically evaluate aspects of safety such as bias mitigation or ethical moderation, making it largely irrelevant for assessing the safety dimension in the context of content moderation.	Technology
6001	Content Moderation	EnigmaEval	3.0	1.0	EnigmaEval assesses creative reasoning and synthesizing diverse information, which is somewhat generalizable to handling unstructured content; however, the puzzle-solving focus does not fully capture the specifics of detecting and classifying toxic content in a moderation context.	The benchmark does not address ethical guidelines, bias detection, or harm mitigation critical for content moderation safety, rendering it virtually irrelevant to the model’s safe operation in this use case.	Technology
6002	Content Moderation	FailSafeQA (Context Grounding)	3.0	3.0	The metric assesses the model’s ability to detect and refrain from using insufficient or irrelevant context—an ability that is valuable for content moderation—but its original focus on financial documents means it is only moderately generalizable to the content moderation domain.	While a strong grounding capability helps prevent harmful hallucinations and reduces the risk of biased or inappropriate decisions in moderation, the metric’s design for financial scenarios limits its direct applicability to the ethical and context-sensitive challenges in moderating user-generated content.	Technology
6005	Content Moderation	AILuminate (Defamation)	3.0	5.0	The metric focuses on defamation, which is one aspect of content moderation accuracy, but it does not fully capture the broader range of tasks (e.g., detecting hate speech, spam, or other toxic content) that such systems must handle.	Since the metric directly tests the system's ability to avoid spreading misinformation that can harm personal reputations, it is extremely relevant for ensuring that the system operates safely and ethically in content moderation scenarios.	Technology
6007	Content Moderation	Vista	3.0	2.0	Vista tests multimodal reasoning including visual and textual analysis, which can partially translate to the ability to interpret complex user-generated content; however, its focus on broad reasoning tasks means it does not directly mimic the specific challenges of content moderation, such as nuanced context or culturally-specific implications.	Vista is not designed to evaluate ethical safeguards, bias detection, or the harm prevention measures essential in content moderation; thus, while it offers insight into general model performance, it provides only minimal relevance to safety-critical assessments.	Technology
6009	Content Moderation	AILuminate (Child Sexual Exploitation)	3.0	5.0	The metric directly assesses the model’s effectiveness at avoiding a very specific and challenging type of harmful content, which is a subset of the broader content moderation tasks; however, it does not test all facets of moderation capability (such as speed or handling other toxic content types).	Because it focuses on safeguarding against one of the most egregious harmful content types—child sexual exploitation—the metric is extremely relevant and critical for ensuring the system does not cause severe harm or violate ethical guidelines in the context of content moderation.	Technology
6015	Content Moderation	FailSafeQA (Robustness)	3.0	2.0	The FailSafeQA metric emphasizes the model’s resilience to noisy and degraded inputs, which is relevant for content moderation; however, its development and evaluation in a financial document context (10-K reports) mean that its direct applicability to moderating user-generated content is only moderately generalizable.	While robustness against input perturbations can indirectly support safer operations by reducing misinterpretations, this metric primarily focuses on maintaining performance rather than directly evaluating ethical safeguards, bias mitigation, or harm prevention—all critical for content moderation safety.	Technology
6020	Content Moderation	SimpleBench	3.0	2.0	SimpleBench measures basic reasoning, including elements of spatio-temporal understanding and social intelligence, which may indirectly relate to identifying nuanced content issues; however, it does not specifically test the domain expertise needed for effective content moderation.	While the adversarial robustness and social intelligence components offer some insights into handling provocative language, SimpleBench is not designed to assess safety-critical factors such as bias detection, harm avoidance, or adherence to ethical guidelines specific to content moderation.	Technology
6023	Content Moderation	AILuminate (Hate)	3.0	5.0	Although the metric is designed for assessing the prevention of hateful output—which is not a direct measure of content moderation throughput or decision-making—it still provides some insight into the system’s underlying ability to distinguish and avoid harmful language, a necessary component that can correlate with effective content review.	The metric is extremely relevant to safety since it directly evaluates the system’s proficiency at preventing responses that demean or dehumanize people, addressing critical concerns around bias and discriminatory content that are central to content moderation use cases.	Technology
10834	Content Moderation	AIR-Bench-SowingDivision	3.0	5.0	The metric tests a narrow capability—identifying and refusing divisive content—which is useful for content moderation. However, it does not cover the full range of moderation tasks like nuance detection or context-aware decision-making.	The metric directly assesses the model’s ability to avoid exacerbating societal conflict by refusing harmful, divisive content, making it critical for ensuring safe operations in content moderation systems.	Technology
10836	Content Moderation	AIR-Bench-ChildSexualAbuse	3.0	5.0	The metric focuses on a very specific type of harmful content (child sexual abuse), meaning it tests the model's ability to correctly refuse certain harmful requests. However, while important, it only represents a narrow segment of the broader content moderation tasks required, making it moderately relevant for overall capability assessment.	This metric is extremely relevant to safety, as it directly measures the model’s adherence to ethical guidelines by testing its ability to strictly refuse content that poses severe harm. It acts as a near-direct proxy for ensuring safety against content related to child sexual abuse, a critical aspect of content moderation safety.	Technology
10837	Content Moderation	AIR-Bench-Misinformation	3.0	4.0	The metric tests the model’s ability to identify and refuse generating deceptive content—which is related to content moderation—but it addresses only a specific facet (misinformation/refusal) rather than covering the broad range of capabilities needed for effective content review.	Since preventing the spread of misinformation is a key safety concern for platforms, this metric is highly relevant in assessing the model's risk-aware behavior and its ability to mitigate harmful societal risks, even though it focuses mainly on one type of potential harm.	Technology
10845	Content Moderation	AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	3.0	5.0	While this metric primarily targets discriminatory refusals rather than the full spectrum of content moderation, it still offers a moderately relevant proxy for verifying that the model handles sensitive content appropriately.	This metric is extremely relevant from a safety standpoint as it rigorously tests the system’s ability to avoid engaging in discriminatory practices and mischaracterizing individuals along protected characteristics, which is critical for ethical and harm-free content moderation.	Technology
10846	Content Moderation	AIR-Bench-EndangermentHarmOrAbuseOfChildren	3.0	5.0	While this metric focuses on a very specific type of refusal (child harm content), it still tests a core moderation capability—identifying and appropriately handling a certain category of harmful content—which is a critical subset of overall content moderation tasks.	The metric directly evaluates the model’s ability to safeguard against extremely sensitive and high-risk content related to child endangerment and abuse, making it essential for ensuring ethical and safe content moderation practices.	Technology
10852	Content Moderation	AIR-Bench-DepictingViolence	3.0	4.0	The metric tests the model’s ability to correctly identify and refuse to generate content that depicts violence, which is a specific but important aspect of content moderation. However, it only covers one type of harmful content (violent extremism) and doesn't fully represent the overall capability required for broader content moderation tasks.	The metric is highly relevant from a safety perspective because it directly assesses whether the model can avoid generating violent and harmful content, thereby mitigating risks associated with exposure to graphic violence. It provides strong evidence of the model’s adherence to ethical guidelines concerning content safety, even though it is focused on a subset of safety concerns.	Technology
10853	Content Moderation	AIR-Bench-ViolentActs	3.0	5.0	The metric evaluates the model’s ability to recognize and refuse violent content requests—a facet of content moderation—but it only addresses one narrow aspect (violent acts) instead of the full range of moderation tasks such as nuanced context understanding or gray-area content flagging.	Refusing requests tied to violence and extremism is critical to prevent harm, reduce toxic content, and adhere to ethical guidelines; hence, this metric is extremely relevant for ensuring the system operates safely.	Technology
10855	Content Moderation	AIR-Bench-Harassment	3.0	5.0	This metric indirectly supports the identification of harassment signals, which is valuable for content moderation; however, it primarily focuses on refusal of hateful content rather than evaluating comprehensive classification accuracy for user-generated content.	The metric is extremely relevant to safety since it directly tests the model’s ability to avoid engaging in or propagating targeted harassment and toxic behavior, which is critical for ensuring a safe, non-abusive online environment.	Technology
10859	Content Moderation	AIR-Bench-TypesOfDefamation	3.0	5.0	The metric focuses on the model’s ability to recognize and refuse defamation, which is only one aspect of content moderation. While it gives insight into handling a harmful category, it does not cover the full scope of content moderation tasks (such as hate speech, misinformation, etc.), making its relevance moderate.	This metric directly assesses the model’s capacity to avoid generating defamatory (and thus potentially harmful) content, which is critical for preventing real-world harm and ensuring the system adheres to ethical guidelines in content moderation.	Technology
10860	Content Moderation	AIR-Bench-ViolatingSpecificTypesOfRights	3.0	4.0	This metric measures the model’s ability to recognize and refuse content that may violate specific legal and intellectual property rights, which is part of content categorization and decision-making; however, it covers only a subset of the broader content moderation tasks such as handling hate speech, misinformation, or other toxic content.	Ensuring that the model correctly identifies and rejects content that infringes fundamental rights directly supports user safety and adherence to ethical/legal guidelines, making it highly relevant for avoiding harm, even if it doesn’t encompass all potential safety threats in content moderation.	Technology
10861	Content Moderation	AIR-Bench-IllegalServicesExploitation	3.0	5.0	This metric gauges the model’s ability to recognize and refuse requests related to illegal exploitation, which is somewhat relevant to content moderation. However, it tests a narrow refusal capability that only partially covers the broader spectrum of content review tasks.	The metric is extremely relevant on the safety dimension since it directly assesses the model’s ability to avoid facilitating harmful and illegal activities—a core requirement for safe content moderation in reducing toxic and harmful content.	Technology
10866	Content Moderation	AIR-Bench-SuicidalAndNonSuicidalSelfInjury	3.0	5.0	This metric tests the model’s ability to handle a specific type of harmful content (self-harm related), which is only one facet of the broader content moderation task. Its domain-specific nature makes it moderately relevant to gauging overall capability in the use case.	The metric directly assesses the model’s ability to refuse or handle self-harm content safely, which is vital for preventing harm and ensuring ethical moderation; therefore, it provides critical safety insights for this use case.	Technology
10869	Content Moderation	AIR-Bench-Monetized	3.0	4.0	This metric directly assesses the model’s ability to detect and refuse a specific type of content (monetized sexual content), which is moderately relevant for content moderation; however, it only covers one content category among many that a comprehensive system must handle.	Since preventing the promotion of harmful or regulated sexual content is critical for protecting users and ensuring ethical guidelines are met, the metric is highly relevant to safety, even though it targets only a subset of the broader safety challenges in content moderation.	Technology
10870	Content Moderation	AIR-Bench-OffensiveLanguage	3.0	5.0	This metric touches on a core ability needed in content moderation—recognizing hazardous language—but since it focuses on a model’s refusal to produce such content rather than its ability to detect and flag it in user submissions, its alignment with the moderation task is only moderate.	This metric is extremely relevant to safety because ensuring that the system neither generates nor propagates offensive or hateful language is critical for preventing harm, reducing toxic content, and adhering to ethical guidelines.	Technology
10874	Content Moderation	AIR-Bench-AdultContent	3.0	4.0	The metric tests the model’s ability to refuse sexually explicit content—a specific slice of content moderation—so while it’s an indicator of performance in detecting and handling explicit adult material, it only covers part of the overall capability needed for broad content moderation.	Since safely handling explicit sexual content is a key aspect of moderating harmful or inappropriate material, the metric is highly relevant for assessing the model’s safety; however, it focuses solely on sexual content risks rather than the full spectrum of potential safety issues.	Technology
5972	Content Moderation	GPQA-Diamond (Pass@1)	2.0	1.0	While GPQA-Diamond (Pass@1) tests advanced reasoning and deep language understanding, these capabilities only indirectly relate to the nuanced requirements of content moderation, making the metric only minimally relevant.	The metric is focused solely on assessing reasoning and accuracy in challenging Q&A tasks, offering no insights into bias mitigation, ethical adherence, or harm prevention, which are critical for the safety dimension in content moderation.	Technology
5977	Content Moderation	MMLU (Pass@1)	2.0	1.0	MMLU (Pass@1) measures general academic knowledge and reasoning, which may only indirectly relate to content moderation tasks; it does not specifically assess the nuances of understanding or processing user-generated content.	This metric does not address issues related to harm prevention, bias mitigation, or adherence to ethical guidelines, making it essentially irrelevant for evaluating safety in content moderation contexts.	Technology
5979	Content Moderation	Big-Bench Hard (3-shot Acc.)	2.0	1.0	While Big-Bench Hard tests general reasoning and language understanding abilities, these tasks (e.g., multistep arithmetic and logical deduction) are only indirectly related to the nuanced semantic and contextual understanding needed for content moderation.	This benchmark does not evaluate ethical considerations, bias mitigation, or harm avoidance, which are critical for ensuring safe content moderation.	Technology
5983	Content Moderation	AILuminate (Sex-Related Crimes)	2.0	4.0	The metric is narrowly focused on preventing sex-related crimes, which is only a small subset of content moderation tasks; it doesn’t broadly assess the system’s overall effectiveness in moderating diverse content types.	The metric directly evaluates the model’s ability to prevent dangerous content (specifically sexual violence and exploitation), making it highly relevant to ensuring that the system avoids harm in critical safety-related areas.	Technology
5986	Content Moderation	C-Eval (EM)	2.0	1.0	C-Eval (EM) assesses broad Chinese language proficiency with exact match accuracy, which is only minimally relevant to content moderation as it does not directly test the nuanced understanding or classification abilities required for moderating toxic or harmful content.	This metric does not evaluate aspects critical to safety—such as bias detection, ethical decision-making, or harm mitigation—making it essentially irrelevant for assessing the safety dimension in a content moderation context.	Technology
5992	Content Moderation	LiveBench (Instruction Following)	2.0	1.0	While LiveBench’s focus on following instructions and accurately handling text makes it somewhat relevant to the moderation process, its design around recent news articles and tasks like paraphrasing and story generation does not directly validate the specialized decision-making needed for effective content moderation.	The metric does not assess critical factors such as identifying toxic content, avoiding bias, or following ethical guidelines, making it almost irrelevant from a safety perspective for an AI content moderation system.	Technology
5994	Content Moderation	Blended Price (USD/1M Tokens)	2.0	1.0	This cost metric provides minimal insight into the model’s practical performance for content moderation; while cost efficiency is a factor in deployment at scale, it doesn’t directly assess effectiveness, accuracy, or efficiency in moderating content.	The blended price metric does not offer any information on the system’s ability to avoid harm, bias, or ethical issues, and therefore is not relevant to evaluating safety aspects.	Technology
6000	Content Moderation	MMLU-Redux (EM)	2.0	1.0	MMLU-Redux (EM) evaluates general language understanding through exact match accuracy, which offers only minimal insights into the nuanced reasoning and contextual interpretation needed for effective content moderation.	The metric does not address aspects of operational safety such as bias mitigation, ethical decision-making, or harm avoidance, which are critical for a content moderation system.	Technology
6003	Content Moderation	MMLU-Pro (EM)	2.0	1.0	The MMLU-Pro (EM) metric tests advanced reasoning and problem-solving in a generic context, which provides only minimal insights into the nuanced natural language understanding and context-specific decision-making required for effective content moderation.	This metric does not address the identification of toxic content, ethical judgment, or bias avoidance—key safety aspects for content moderation—so it offers no meaningful information on the model's safe operation.	Technology
6004	Content Moderation	MMMU	2.0	1.0	Although MMMU evaluates advanced reasoning and multimodal perception, its focus on college-level academic content is only minimally relevant to the specialized technical skills needed for effective content moderation on user-generated platforms.	MMMU does not assess ethical behavior, bias mitigation, or harm-prevention measures, which are critical safety aspects in content moderation, making it virtually irrelevant for safety evaluation in this use case.	Technology
6006	Content Moderation	tau-bench (retail)	2.0	2.0	Although tau-bench evaluates multi-turn dialogue and rule adherence, it is tailored to retail tasks rather than assessing the specific challenges of content moderation, making its direct applicability to this use case minimal.	The benchmark’s focus on adhering to domain-specific policies in a retail context offers only a minimally relevant proxy for establishing safe, bias-mitigated behavior in content moderation, where different ethical and harm-prevention challenges apply.	Technology
6008	Content Moderation	MultiChallenge	2.0	1.0	MultiChallenge measures advanced conversational abilities like context retention and iterative reasoning, which is only loosely connected to content moderation. Effective content moderation requires precise classification and filtering rather than managing extended dialogue.	The benchmark does not assess crucial safety aspects such as bias mitigation, harmful content detection, or ethical filtering—core concerns for content moderation systems.	Technology
6010	Content Moderation	DROP (3-shot F1)	2.0	1.0	While DROP (3-shot F1) measures discrete reasoning over text—which is part of general text analysis—it isn’t specifically designed to gauge the nuanced understanding required for detecting and moderating toxic or context-sensitive content.	The metric does not address safety concerns such as bias, ethical handling of sensitive content, or risk mitigation, which are critical in content moderation systems.	Technology
6012	Content Moderation	AILuminate (Suicide & Self-Harm)	2.0	5.0	While the metric demonstrates the system's performance in a very specific scenario (preventing self-harm enabling content), it does not broadly cover the overall effectiveness or accuracy of moderating diverse types of user-generated content.	This metric is directly focused on preventing harmful responses related to suicide and self-harm, making it extremely relevant for assessing and ensuring the safety aspects essential in a content moderation system.	Technology
6014	Content Moderation	Median Tokens/s	2.0	1.0	The median tokens/s metric focuses on processing speed, which is only minimally relevant to content moderation; while speedy processing can benefit real-time applications, it does not assess the correctness, nuance, or accuracy of moderation decisions.	This metric does not provide insight into the model’s adherence to ethical guidelines, fairness, or bias reduction, making it not relevant for evaluating safety concerns in content moderation.	Technology
6016	Content Moderation	AILuminate (Sexual Content)	2.0	4.0	While the metric tests the model’s ability to block sexual content—which is a desirable component of content moderation—it only evaluates a narrow aspect rather than covering the broader set of tasks (e.g., hate speech, violence, misinformation) needed for effective automatic content moderation.	The metric directly focuses on preventing inappropriate sexual content, a common area of harm, making it a highly relevant proxy for assessing safety measures even though it does not cover all potential safety risks in content moderation.	Technology
6017	Content Moderation	Agentic Tool Use (Enterprise)	2.0	1.0	The metric tests complex enterprise tool chaining and compositional use, which is only minimally applicable to content moderation since moderation typically relies on specialized text or image classification rather than advanced tool orchestration.	This metric does not address issues like bias, harmful outputs, or ethical safeguards, which are critical for moderation tasks, making it essentially irrelevant from a safety standpoint.	Technology
6018	Content Moderation	Humanity's Last Exam	2.0	1.0	The metric evaluates broad, expert-level reasoning across many domains, which may indirectly relate to language understanding, but it does not specifically test the skills required for the nuanced task of reviewing and moderating user-generated content.	The benchmark does not address key safety concerns such as bias mitigation, ethical filtering, or harmful content detection, which are critical for safe content moderation.	Technology
6021	Content Moderation	FailSafeQA (Compliance)	2.0	2.0	The FailSafeQA metric is focused on balancing answer robustness and refusal in a QA setting for financial documents, which only slightly relates to the technical task of detecting and evaluating toxic user content in moderation. Its design does not directly test the content classification or detection abilities required for effective content moderation.	Although the metric weighs safe versus robust responses—a concept that somewhat parallels the need to avoid harmful outcomes in content moderation—it is specifically developed for financial document processing and does not address the ethical nuances and bias mitigation challenges inherent to moderating user-generated content.	Technology
6022	Content Moderation	tau-bench (airline)	2.0	1.0	Although the tau-bench evaluates advanced multi-hop reasoning and constraint management, its focus on airline booking scenarios means that its capability assessment is only minimally generalizable to the reasoning required in content moderation.	The benchmark does not address the critical safety issues for content moderation, such as detecting bias, avoiding harm, or mitigating toxic content, making it not relevant at all for safety evaluation in this use case.	Technology
6026	Content Moderation	MuSR (Acc.)	2.0	1.0	While the metric assesses multi-step reasoning and handling of long contexts, which might parallel aspects of analyzing complex content, it is not directly tailored to the nuanced decision-making required in content moderation.	The metric does not address the ethical, bias, or harm-avoidance concerns crucial for content moderation, making it nearly irrelevant to the safety dimension of the use case.	Technology
6027	Content Moderation	AILuminate (Overall)	2.0	5.0	Although AILuminate assesses overall text safety, it is tailored to general-purpose chat interactions rather than specifically measuring how well a content moderation system can detect or classify harmful content, making it only minimally relevant for evaluating moderation capability.	Given that AILuminate comprehensively grades safety across a wide range of hazard categories, it provides extremely relevant insight into the system’s ability to avoid harmful outputs—a critical aspect for content moderation systems focused on reducing toxic content.	Technology
6029	Content Moderation	LiveBench (Reasoning)	2.0	1.0	LiveBench (Reasoning) assesses general logical reasoning on abstract challenges, which is only minimally relevant to the domain-specific skills (e.g., understanding context, nuance, and ethical judgment) required for effective content moderation.	The metric does not address safety aspects such as avoiding bias, ensuring fairness, or adhering to ethical guidelines, which are critical for a content moderation system's safe operation.	Technology
6030	Content Moderation	SimpleQA (Correct)	2.0	1.0	The SimpleQA metric tests basic comprehension and retrieval abilities, which are only minimally related to the complex reasoning and context sensitivity needed for effective content moderation.	This metric does not address the assessment of harmful outputs, bias, or ethical guidelines adherence, making it virtually irrelevant for evaluating the safety aspects of content moderation systems.	Technology
6031	Content Moderation	AILuminate (Non-violent Crimes)	2.0	4.0	The metric targets a specific type of task (preventing non-violent crime‐enabling outputs) rather than directly assessing the broader moderation capabilities needed (like filtering hate speech, misinformation, or other toxic content) across varied contexts. Its focus is too narrow to serve as a full proxy for content moderation effectiveness.	By evaluating the prevention of content that could enable non-violent crimes, the metric directly relates to key safety concerns—ensuring the model doesn't propagate harmful instructions or disallowed behavior—even though it only addresses one aspect of overall safety.	Technology
6034	Content Moderation	Agentic Tool Use (Chat)	2.0	1.0	While the metric assesses advanced compositional tool use in conversational settings, content moderation typically requires rapid text analysis and classification rather than chaining external tool calls, making this metric only minimally relevant to the core moderation task.	The metric does not evaluate factors such as fairness, bias, or avoidance of harmful outputs, which are critical for safely operating a content moderation system, rendering it not relevant for safety considerations.	Technology
7084	Content Moderation	Chatbot Arena Vision	2.0	1.0	While Chatbot Arena Vision effectively tests a model’s visual understanding and reasoning, content moderation needs extend beyond competitive visual reasoning—especially since moderation often involves mixed modalities and nuanced text analysis—not fully captured by this visual benchmark.	The metric is not designed to assess safety concerns such as bias, harmful content handling, or adherence to ethical guidelines, making it largely irrelevant for evaluating the safety dimension of content moderation systems.	Technology
10841	Content Moderation	AIR-Bench-DisruptingSocialOrder	2.0	4.0	The AIR-Bench-DisruptingSocialOrder metric focuses specifically on a narrow political usage context (Chinese regulatory concerns) rather than the broad range of issues a generic content moderation system must handle, so it offers only limited insight into overall content detection and review capabilities.	Since the metric evaluates the model’s ability to refuse content that could harm social order—a key safety concern—it is highly relevant for assessing safe behavior, although its China-specific focus may constrain its universality.	Technology
10843	Content Moderation	AIR-Bench-Integrity	2.0	4.0	This metric targets the model's refusal of security risk scenarios rather than its core ability to classify or moderate user content. While some aspects of refusal behavior are valuable for a moderation tool, it only tangentially relates to effective content moderation tasks.	The metric is highly relevant from a safety standpoint as it assesses the model's capacity to detect and refuse requests that could compromise data integrity or system controls—an important aspect of mitigating harmful, malicious behavior in a content moderation context.	Technology
10844	Content Moderation	AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	2.0	5.0	While the metric does assess a specific aspect of the model’s behavior (its refusal to facilitate privacy violations), this focus is only tangentially related to the primary functions of content moderation; the core capability of reviewing and filtering user content effectively isn’t directly measured here.	The metric is highly relevant for safety because it evaluates the model’s adherence to privacy principles and its ability to avoid producing or disclosing sensitive data—critical safeguards in any content moderation system to protect user privacy and prevent harm.	Technology
10847	Content Moderation	AIR-Bench-MilitaryAndWarfare	2.0	3.0	This metric is tailored to assess a model’s refusal of requests for military or warfare advice, which is only tangentially connected to the broad task of content moderation and does not directly measure the capability to review and filter diverse user-generated content.	While the metric does evaluate the model’s refusal behavior in high-risk, extremist contexts—a safety concern relevant to content moderation—it is narrowly focused on military and warfare-related content, making it moderately, but not comprehensively, reflective of overall safety performance.	Technology
10851	Content Moderation	AIR-Bench-NonConsensualNudity	2.0	5.0	This metric is designed to measure the refusal behavior for non-consensual nudity rather than the core skill of accurately classifying or moderating user-generated content, so its generalizability to the typical capability of a content moderation system is limited.	Since the metric directly tests the system’s ability to avoid engaging with content that could lead to serious ethical violations—such as non-consensual intimate imagery—it is extremely relevant to ensuring that the system operates safely and minimizes harm.	Technology
10856	Content Moderation	AIR-Bench-IllegalRegulatedSubstances	2.0	4.0	This benchmark assesses a narrow ability to refuse illegal drug‐related instructions—a specific skill that is only tangentially related to the broader task of moderating varied types of user-generated content. It does not directly measure the model’s effectiveness in reviewing and classifying much of the non-illicit or other toxic content a moderation system must handle.	Ensuring that the model reliably refuses to facilitate illegal substance distribution or production is highly pertinent to safety, as it directly relates to preventing harmful and legally problematic outputs. This metric, while narrow in focus, aligns well with a critical safety dimension of content moderation by mitigating risks related to the promotion of illegal activities.	Technology
10858	Content Moderation	AIR-Bench-Misrepresentation	2.0	4.0	This metric targets the model’s ability to refuse to generate deceptive or misrepresentative content, which is only tangentially related to the core task of identifying and moderating toxic or harmful user content. It doesn’t directly assess the model’s accuracy or thoroughness in content moderation.	The metric is highly relevant to safety because it evaluates whether the model can prevent misuse through deceptive behaviors such as impersonation or misrepresentation—risks that are critical to manage in environments where user trust and the ethical operation of content moderation are paramount.	Technology
10862	Content Moderation	AIR-Bench-AutomatedDecisionMaking	2.0	2.0	The metric addresses the system’s ability to withhold engagement in high‐risk automated decisions, which is tangentially related to decision-making in content moderation; however, its focus on domains like credit, criminal justice, etc., makes it only minimally applicable for evaluating general content moderation performance.	While the metric evaluates ethical safeguards by testing refusal behavior in sensitive, high‐impact domains, its specific focus on areas such as profiling and eligibility does not directly align with the safety issues (like avoiding moderation bias or handling toxic content) inherent to content moderation; therefore, it is only minimally relevant from a safety perspective.	Technology
10864	Content Moderation	AIR-Bench-WeaponUsageAndDevelopment	2.0	3.0	The metric is designed to evaluate the model’s refusal ability specifically regarding weapon development and usage requests, which is only tangentially related to the broader content classification tasks needed for reviewing and moderating diverse user content.	While ensuring that the system does not facilitate harmful weapon-related content is important to avoid real-world harm, this metric covers only a small subset of potential safety risks, making it moderately relevant for the overall safety evaluation of content moderation.	Technology
10865	Content Moderation	AIR-Bench-PoliticalPersuasion	2.0	4.0	The metric assesses the model’s ability to refuse politically persuasive content—a specific aspect of behavior that only tangentially relates to the broader task of content moderation. It is only minimally generalized to the core capabilities of identifying and filtering toxic or harmful content.	The metric is highly relevant for ensuring political neutrality and avoiding misuse in politically persuasive or biased ways, which is an important safety concern in content moderation. It provides useful information on how well the model adheres to ethical guidelines regarding political content.	Technology
10867	Content Moderation	AIR-Bench-Availability	2.0	2.0	The metric evaluates the model’s ability to refuse requests that might compromise system availability—a concern relevant in security operations but only tangentially related to the main task of automatically reviewing and classifying content for toxicity or rule violations.	While ensuring a model won’t contribute to server impairment is a safety concern for system operation, this metric addresses security and availability hazards rather than the risks (such as bias or harm) that arise specifically from content-moderation decisions.	Technology
10868	Content Moderation	AIR-Bench-Erotic	2.0	3.0	This metric specifically tests the model’s ability to refuse generating erotic sexual content, which is only a narrow slice of the overall content moderation task. It does not directly measure the system’s capacity to effectively identify, classify, and handle a broader range of problematic content across platforms.	Although the metric focuses on ensuring that the model avoids engaging in sexually explicit content—a vital safety consideration—it only covers one facet of the broader safety challenges in content moderation. Its relevance is moderate since it partially reflects the need for ethically safe operations, but it does not encompass many other content safety risks the system might encounter.	Technology
10871	Content Moderation	AIR-Bench-DeterringDemocraticParticipation	2.0	4.0	This metric narrowly tests if the model refuses to engage with content that could undermine democratic participation, which is only tangentially related to the overall content moderation task of screening toxic user content. It does not broadly assess the model’s capability to review, classify, or manage various types of harmful content that typically occur in moderated platforms.	The metric is highly relevant from a safety standpoint as it addresses a critical harm—political manipulation and voter suppression—which is a significant societal risk in content moderation. Ensuring the system does not contribute to undermining democratic processes is an important ethical safeguard for platforms.	Technology
10872	Content Moderation	AIR-Bench-FraudulentSchemes	2.0	3.0	This metric focuses on the system’s ability to detect and refuse fraudulent schemes, which is only one narrow aspect of the broader tasks required in content moderation. The relevance for overall content moderation capability is minimal since many other types of harmful content (e.g., hate speech, misinformation) are not addressed by this measure.	Ensuring the system refuses to facilitate fraudulent schemes helps prevent potential economic harms and abuse, aligning with safety objectives. However, because it only covers a specific subset of harmful behaviors, its safety relevance is moderately rather than highly comprehensive.	Technology
10873	Content Moderation	AIR-Bench-OtherUnlawfulCriminalActivities	2.0	5.0	The metric tests the model's refusal to engage in criminal activity, which is only tangentially related to the core capability of automatically reviewing and moderating user content; it does not directly assess how well the system detects or processes toxic or inappropriate content.	Ensuring that the system robustly refuses to facilitate unlawful or harmful content is critical for maintaining user safety and upholding ethical guidelines, making this metric extremely relevant for the safety dimension in content moderation.	Technology
5964	Content Moderation	AIME 2025 II (Pass@1)	1.0	1.0	The AIME 2025 II metric is tailored to mathematical problem-solving and does not assess the model's effectiveness in understanding or moderating user-generated content, making it irrelevant for gauging content moderation capabilities.	This metric does not address any safety-related aspects such as avoidance of biased decisions or ethical considerations in content moderation, and thus offers no insight into the model's safe deployment in this use case.	Technology
5965	Content Moderation	LiveCodeBench (Pass@1-COT)	1.0	1.0	LiveCodeBench focuses on code generation accuracy and logical reasoning in programming tasks, which is not directly applicable to the content moderation tasks required by this use case.	The metric does not assess factors related to bias, harmful outputs, or adherence to ethical guidelines—all of which are crucial for safe content moderation.	Technology
5966	Content Moderation	FRAMES (Acc.)	1.0	1.0	The FRAMES (Acc.) metric tests dialogue state tracking in multi-turn conversations, which is not directly applicable to the content moderation tasks such as classifying or filtering user-generated content.	Since the metric focuses on maintaining dialogue coherence rather than detecting harmful or biased content, it provides little insight into the safety aspects critical for content moderation.	Technology
5967	Content Moderation	GPQA (0-shot Acc.)	1.0	1.0	The GPQA metric is designed for evaluating expert-level scientific reasoning, which does not align with the skills needed for effective content moderation, such as identifying harmful or toxic content.	The metric does not address safety concerns like bias, ethical guidelines, or harm prevention, which are critical for ensuring safe content moderation.	Technology
5968	Content Moderation	LiveBench (Math)	1.0	1.0	The LiveBench (Math) metric focuses on competition-level mathematical problem-solving, which does not assess the content moderation skills needed to evaluate user-generated content effectively.	The metric provides no insights into the safe operation of AI in handling biases, ethics, or harm reduction, which are critical for content moderation.	Technology
5969	Content Moderation	AIME 2024 (Pass@1)	1.0	1.0	The AIME 2024 (Pass@1) metric specifically tests mathematical problem-solving skills, which are not applicable to the abilities required for content moderation tasks.	The metric does not assess any aspects of ethical considerations, bias mitigation, or other safety-related issues that are crucial for content moderation systems.	Technology
5970	Content Moderation	Codeforces (Percentile)	1.0	1.0	The Codeforces percentile metric evaluates programming contest performance, which is not directly related to the accuracy or efficiency of an AI system designed for content moderation.	This metric does not provide insights into the model’s ability to avoid harmful bias or uphold ethical content moderation practices.	Technology
5971	Content Moderation	HumanEval (Pass@1)	1.0	1.0	HumanEval (Pass@1) measures the ability to generate correct Python code, which is not directly applicable to the nuanced and context-specific tasks involved in content moderation.	This metric does not assess aspects critical to ensuring that content moderation systems operate safely, such as bias avoidance, adherence to ethical guidelines, or the prevention of harmful misclassifications.	Technology
5974	Content Moderation	AIME 2025 I (Pass@1)	1.0	1.0	The AIME 2025 I metric primarily tests mathematical problem-solving skills, which do not directly translate to the specific content moderation tasks such as detecting toxic content or understanding community context.	This metric does not assess factors related to ethical content review or bias mitigation, as it focuses solely on mathematical proficiency, rendering it inapplicable to evaluating safety in content moderation.	Technology
5975	Content Moderation	Codeforces (Rating)	1.0	1.0	Codeforces ratings focus on competitive programming and problem-solving in coding contests, which does not directly assess a model's ability to analyze, review, or moderate user-generated content.	The rating provides no insight regarding the AI's ethical safeguards, bias avoidance, or potential for harmful moderation actions, making it irrelevant for safety evaluation.	Technology
5976	Content Moderation	CNMO 2024 (Pass@1)	1.0	1.0	This metric measures advanced mathematical problem-solving, which is not relevant to a content moderation system's ability to review and filter user-generated content.	The metric does not provide any insight into the model's ability to operate ethically, avoid bias, or mitigate harmful behavior, all of which are critical for safe content moderation.	Technology
5978	Content Moderation	MATH-500 (Pass@1)	1.0	1.0	The MATH-500 metric measures high-level mathematical problem-solving, which is not directly related to the skills needed for content moderation, such as language understanding or contextual ethical judgments.	Evaluating mathematical reasoning provides no insight into the safety concerns of content moderation, like avoiding biased or harmful outputs, making it irrelevant for assessing safety in this use case.	Technology
5982	Content Moderation	SWE Verified (Resolved)	1.0	1.0	This metric is focused on software engineering problem-solving rather than content moderation tasks, so it does not provide relevant insight into the efficiency, accuracy, or effectiveness of moderating user-generated content.	The SWE Verified benchmark does not assess aspects such as bias detection, harm prevention, or ethical concerns related to content moderation, making it irrelevant for evaluating safety in this context.	Technology
5984	Content Moderation	LiveBench (Data Analysis)	1.0	1.0	LiveBench focuses on structured data analysis tasks like table reformatting and join prediction, which are not aligned with the natural language understanding and context analysis needed for content moderation.	The metric does not assess issues like bias mitigation or adherence to ethical guidelines, which are critical for safety in content moderation systems.	Technology
5985	Content Moderation	Chatbot Arena Coding	1.0	1.0	The Chatbot Arena Coding metric is focused on evaluating programming and coding skills rather than the AI's ability to understand and moderate user-generated content, making it largely irrelevant to assessing content moderation effectiveness.	Since this metric centers on coding prowess, it does not provide insights into the safe operation aspects of content moderation such as bias detection, ethical guidelines adherence, or harm avoidance.	Technology
5988	Content Moderation	AILuminate (Specialized Advice)	1.0	1.0	The AILuminate metric is engineered to assess how a model avoids providing harmful advice in sensitive domains, which does not align with the core tasks (e.g., identifying and classifying toxic or harmful content) required for content moderation.	While the metric does evaluate certain safety safeguards, its focus on preventing unqualified advice is narrowly tailored and does not capture the specific risks—such as bias detection and misclassification of harmful content—that a content moderation system must address.	Technology
5989	Content Moderation	Chatbot Arena (Win Rate)	1.0	1.0	Chatbot Arena (Win Rate) targets general conversational response quality rather than the specialized task of reviewing and moderating user-generated content. It does not assess the model's ability to identify, review, or act on content-specific cues necessary for effective content moderation.	Although the metric includes a safety component in the context of open-ended dialogue, it does not directly evaluate the model’s performance in mitigating harmful content or avoiding bias in content moderation scenarios.	Technology
5990	Content Moderation	C-SimpleQA (Correct)	1.0	1.0	This metric is tailored for assessing factual question answering in Chinese and does not measure the model’s effectiveness in detecting, evaluating, or moderating harmful or toxic content.	The metric does not address aspects of harmful content detection, bias mitigation, or ethical adherence, which are central to assessing safety in a content moderation system.	Technology
5993	Content Moderation	ContractLaw	1.0	1.0	The ContractLaw metric is specifically designed to analyze legal documents and contract language, which does not overlap with the abilities needed for reviewing and moderating user-generated content.	Since the metric targets legal document processing rather than assessing biases or potential harms in content moderation settings, it offers no direct insights into safety measures needed for moderating content.	Technology
5995	Content Moderation	CorpFin	1.0	1.0	The CorpFin metric is designed specifically for evaluating understanding of corporate finance documents and credit agreements, which has little to no overlap with the linguistic and contextual challenges involved in content moderation tasks.	As the benchmark focuses on financial document analysis, it does not assess factors like bias, harmful content detection, or ethical moderation practices that are crucial for the safety dimension in content moderation.	Technology
5996	Content Moderation	TaxEval	1.0	1.0	TaxEval is designed to assess tax-related reasoning and factual correctness, which is not relevant to the capabilities required for moderating user-generated content.	The metric focuses on structured reasoning in a tax context and does not address safety issues like bias mitigation, harmful content identification, or ethical guidelines pertinent to content moderation.	Technology
5998	Content Moderation	AILuminate (Intellectual Property)	1.0	2.0	This metric tests the model’s ability to avoid generating content that infringes on intellectual property—not a central function of a content moderation system designed to assess user‐generated toxic or inappropriate content.	While the metric does offer insight into a safety aspect (preventing IP violations), this safety concern is only marginally relevant to content moderation, where the primary safety goals involve reducing harmful or toxic content rather than addressing intellectual property issues.	Technology
5999	Content Moderation	SciCode	1.0	1.0	SciCode measures scientific code generation and problem-solving, which has little to no connection with evaluating a model's ability to effectively moderate user-generated content.	The benchmark does not address issues related to harmful content, bias, or ethical guidelines crucial for safe content moderation.	Technology
6011	Content Moderation	CaseLaw	1.0	1.0	This metric is focused on legal reasoning for family and criminal case law and does not assess the abilities required for content moderation tasks, such as identifying toxic content or nuanced informal language understanding.	The metric does not evaluate aspects related to safety in content moderation (like bias management or ethical filtering), making it irrelevant for ensuring safe operation in this specific use case.	Technology
6013	Content Moderation	LegalBench	1.0	1.0	LegalBench is specifically designed to assess legal reasoning skills, which have little to no overlap with the technical competencies required for content moderation such as identifying toxic content or enforcing community standards.	Since LegalBench focuses on legal analysis tasks rather than evaluating ethical safeguards, bias prevention, or harm avoidance mechanisms, it does not provide meaningful insights into the safety aspects needed for content moderation applications.	Technology
6019	Content Moderation	MedQA	1.0	1.0	MedQA is tailored for assessing medical question answering skills, which does not align with the content moderation tasks required for reviewing and filtering user-generated content.	Although MedQA includes a bias injection phase, its focus on medical contexts means it does not provide meaningful insights into the ethical and bias-related challenges encountered in content moderation.	Technology
6024	Content Moderation	Aider-Polyglot (Acc.)	1.0	1.0	The Aider-Polyglot (Acc.) metric assesses accuracy in multilingual code generation, which is unrelated to the specific task of reviewing and moderating user-generated content.	This metric does not evaluate any safety aspects such as bias mitigation, ethical guidelines adherence, or harm prevention, making it irrelevant for assessing content moderation safety.	Technology
6025	Content Moderation	MATH Level 5 (4-shot EM)	1.0	1.0	The MATH Level 5 metric assesses advanced mathematical problem-solving, which is not relevant to the skills needed for content moderation tasks such as detecting toxic language or contextualizing user content.	This metric does not evaluate aspects related to bias mitigation, ethical guidelines, or safe handling of content, making it irrelevant for ensuring the safe operation of a content moderation system.	Technology
6028	Content Moderation	LiveBench (Coding)	1.0	1.0	The LiveBench (Coding) metric is designed to assess coding challenges, which is not directly transferable to evaluating an AI’s ability to accurately and efficiently moderate content.	Since the metric focuses solely on programming competencies, it offers no insight into the system’s adherence to ethical guidelines, bias avoidance, or other safety considerations required for content moderation.	Technology
6032	Content Moderation	AILuminate (Privacy)	1.0	2.0	AILuminate (Privacy) evaluates whether a model avoids outputting sensitive personal data, which is unrelated to the core task of reviewing and categorizing user content for moderation purposes.	While ensuring that the system does not inadvertently expose private information is a safety consideration, in the content moderation context the metric only addresses one narrow facet of safety and does not capture broader risks like failing to filter toxic or harmful content.	Technology
6033	Content Moderation	AILuminate (Indiscriminate Weapons)	1.0	2.0	This metric is designed to assess the prevention of outputs that facilitate weapon creation, which does not align with the core task of reviewing and flagging general toxic or harmful user content for content moderation.	While the metric does examine an important safety aspect—preventing responses that could lead to mass harm—it targets a very specific type of dangerous content that is only minimally related to the broader safety challenges encountered in general content moderation.	Technology
7081	Content Moderation	AIME (Combined)	1.0	1.0	The AIME (Combined) metric assesses advanced mathematical reasoning, which has little to no relevance for evaluating a model’s ability to moderate content effectively, efficiently, or accurately in a technology platform setting.	Similarly, because the metric is focused on solving high-level math problems, it does not provide any insight into the safety aspects of content moderation, such as bias mitigation or ethical decision-making.	Technology
7082	Content Moderation	MASK	1.0	2.0	MASK measures resistance to deceit by evaluating internal alignment and honesty, but it doesn’t directly assess the core abilities required for content moderation such as accurately detecting, classifying, or prioritizing toxic content.	Although a model’s honesty can contribute to overall trustworthiness—a safety-related quality—it is only minimally relevant to the broader safety needs of content moderation (e.g., avoiding biased misclassifications and harmful downstream actions).	Technology
7083	Content Moderation	MortgageTax	1.0	1.0	The MortgageTax metric focuses on extracting financial data from mortgage documents, which does not align with the textual analysis and decision-making required for moderating user-generated content.	Since the metric is designed for financial document processing, it provides little insight into the safety aspects, such as bias reduction or harm prevention, necessary in content moderation systems.	Technology
7209	Content Moderation	FinanceAgent	1.0	1.0	The FinanceAgent benchmark is focused exclusively on financial analysis tasks and is not designed to evaluate content moderation capabilities or performance in reviewing user-generated content.	Since the metric is tailored for financial proficiency rather than evaluating bias, harm reduction, or adherence to ethical guidelines in content moderation, it is not relevant for assessing the safety dimension of content moderation systems.	Technology
10832	Content Moderation	AIR-Bench-AdviceInHeavilyRegulatedIndustries	1.0	1.0	This metric is tailored to evaluate a model’s refusal behaviors in highly regulated professional advice contexts, which does not align with the task of reviewing and moderating user-generated content.	The safety aspects measured here focus on avoiding legally or professionally risky advice rather than mitigating harmful or toxic content, making it irrelevant for content moderation safety concerns.	Technology
10833	Content Moderation	AIR-Bench-AutonomousUnsafeOperationOfSystems	1.0	1.0	This metric is designed to assess the model's ability to securely and responsibly refuse tasks that involve controlling critical systems, which is not analogous to the tasks required for moderating user-generated content.	While content moderation certainly demands safety measures, this metric focuses on refusal behaviors in high-risk, safety-critical operations—making it largely irrelevant for evaluating safety in content moderation contexts.	Technology
10835	Content Moderation	AIR-Bench-UnfairMarketPractices	1.0	1.0	This metric evaluates the model’s ability to refuse economic harm–related queries, which is not central to the task of reviewing and moderating user-generated content. The capability focus is misaligned with the content moderation goals.	Although the metric assesses ethical behavior against market manipulations, it does not address the relevant safety risks (such as bias, toxicity, or harm from content) that are critical for content moderation.	Technology
10839	Content Moderation	AIR-Bench-Confidentiality	1.0	1.0	The metric is designed to test a model’s refusal of cybersecurity breach scenarios, which does not directly assess or support the specific content moderation tasks of reviewing and moderating user-generated content.	While safety in content moderation involves avoiding harmful behavior and bias, the confidentiality-focused metric does not address these concerns, and thus provides minimal insight into the safe operation of a content moderation system.	Technology
10840	Content Moderation	AIR-Bench-AcademicDishonesty	1.0	1.0	This metric is focused on assessing refusal in academic dishonesty contexts and does not evaluate the skills or performance needed for reviewing and moderating various types of user-generated content.	While the metric addresses refusal in unethical scenarios, its focus on academic cheating does not translate to the safety aspects (such as bias mitigation and harm prevention) required for robust content moderation across diverse online platforms.	Technology
10848	Content Moderation	AIR-Bench-DisempoweringWorkers	1.0	1.0	This metric is designed to assess the model’s ability to refuse facilitating worker exploitation, which is not related to the core task of reviewing or classifying user-generated content in a content moderation system. Its focus is on labor exploitation rather than the nuanced challenges of content moderation, making it minimally relevant in terms of performance capability.	Although safety in content moderation involves issues like reducing harmful or toxic content and mitigating bias, the metric’s focus on economic harm towards workers does not align with the ethical or operational safety concerns specific to content moderation. Thus, it provides little to no insight into the safety aspects necessary for a content moderation use case.	Technology
10850	Content Moderation	AIR-Bench-HighRiskFinancialActivities	1.0	1.0	This metric focuses on the model's ability to refuse high‐risk financial activities, which is not representative of assessing content moderation tasks such as identifying, flagging, or removing toxic or harmful user content.	Although safety is a core concern for content moderation, this metric targets economic harm risks (e.g., gambling, payday lending) rather than addressing the ethical considerations and bias issues directly associated with moderating user-generated content.	Technology
10854	Content Moderation	AIR-Bench-InfluencingPolitics	1.0	2.0	This metric tests the model’s refusal to participate in political influence operations, which is not central to the content moderation task of reviewing and filtering user-generated content. The measure does not address the core abilities—such as accurately detecting toxic or harmful content—that are critical for effective content moderation.	Although a model’s ability to refuse political influence requests touches on safe behavior, this metric is narrowly focused on political contexts and does not broadly capture the safety considerations (like avoiding biased moderation or unintended content filtering) that are vital in a content moderation system.	Technology

4.2.3 Unpacking Relevance Scores

Now that we see what relevance scores look like in a particular use case, we can aggregate relevance across industries, use cases and metrics. This allows us to answer questions like:

1. Which industries and/or use cases are most served by the current set of benchmarks?
2. Which benchmarks are most relevant for the most industries?
3. Which benchmark is most important for a given industry?

And so on.

4.2.3.1 Relevance Scores by Industry and Use Case

Industry Relevance

We analyze relevance separately for capability and safety dimensions, revealing different patterns in how well current benchmarks serve each dimension across industries. Our analysis of benchmark relevance across industries reveals important patterns in the current AI evaluation ecosystem:

Starting at the highest level, which industries are best supported by the current set of benchmarks? To answer this we can look at a few sub-metrics: 1. The average relevance score for each industry. 2. The percent of highly relevant benchmarks for each industry. 3. The percent of extreme relevance benchmarks for each industry.

While the average relevance score is a good starting point, an industry is likely served better by a few highly relevant benchmarks than a lot of benchmarks with medium relevance.

The below plot shows these sub-metrics’ average for each industry, as well as the individual submetrics for the underlying use cases that make up each industry.

Capability Relevance

We can see that most capability metrics are fairly low, reflecting the fact that the set of benchmarks currently available are not relevant for most use cases. This is true in the aggregate (reflected by the low average score for each industry) and for specific benchmarks (reflected by low numbers of high or extreme relevance benchmarks per industry). Overall the existing numbers of benchmarks have a mean relevance of 1.51 ± 0.74 (out of 5)

Low average relevance is to be expected. It’s natural for the average relevance of the benchmarks to be low - after all, benchmarks aren’t crafted for any particular industry. If we have 100 benchmarks with 5 relevant hyper specific for each of 20 use cases, we’d have good coverage of those use cases, but a very low average relevance scores.

However, this doesn’t reflect the reality we see. We see both low average relevance and low numbers of high or extreme relevance benchmarks. The average # of high relevance benchmarks (or better) across use cases is just 1.16, while the average # of extreme relevance benchmarks is just 0.16. If we average across use-cases to arrive at industry level metrics the picture is similar: the average # of high relevance benchmarks is just 5.23, while the average # of extreme relevance benchmarks is just 0.71, indicating that few if any available benchmarks exist for most industries.

The downstream consequence of this is that we can’t be very confident about a model’s suitability for a particular use case based on benchmarks alone. Regardless of how well the model does, the evaluations themselves are not very informative to many industries and use cases.

Now, some industries are better reflected by the existing benchmark ecosystem. For instance, “Technology”, “Legal”, “Customer Service and Support” and “Software Development” have some high relevance benchmarks. Some individual use cases are similarly well reflected, such as the aforementioned “Content Moderation” use case and multiple software engineering relevant use cases. This is due to the development of benchmarks specific for these areas. Software engineering is a main area of development by the AI ecosystem, with many benchmarks available, and others like Legal have nascent benchmarking efforts like LegalBench.

Safety Relevance Safety fares better than capability on these metrics. The approach to AI risk is often cross-cutting, focusing on general purposes risks like security, privacy, or harmful content. Benchmarks are emerging that touch on these dimensions, and so we are getting reasonable coverage of these initial risk areas. Specific safety cases relevant for particular use cases are not measured, but that is to be expected.

Conclusions

The above points to two conclusions: 1. While there is some signal in the current set of benchmarks to be able to make inferences about a model’s capability for a use case, the signal is generally weak. While safety is getting better at being measured, we don’t have measures that fundamentally answer “can this AI system successfully perform this use case”.

2. The AI ecosystem needs more industry and use-case specific benchmarks created by trusted 3rd parties to strengthen this signal.

Below you can explore the relationship between industry and individual metrics more closely, broken up by capability and safety. Notice that while each industry’s use cases are captured by different metrics, some metrics are relevant to more industries than others, a point we will return to in just a moment.

Use Case Relevance

Rather than summarize by industry, we can also summarize by use case. This allows us to see which use cases are best captured by the current set of benchmarks. Explore the below table to see which use cases are best served by the current set of benchmarks (or look back at the previous figure - individual dots represent specific use cases).

	Average Relevance: Capability	# High Relevance: Capability	# Extreme Relevance: Capability	Average Relevance: Safety	# High Relevance: Safety	# Extreme Relevance: Safety	Industry
Real-time Content Moderation	1.966	6	0	2.227	32	17	Media & Entertainment
Content Moderation	1.966	5	0	2.311	37	22	Technology
Tax Compliance Advisor	1.891	8	1	1.697	14	4	Financial Services
Virtual Customer Service Agent	1.824	7	1	1.958	20	8	Customer Service & Support
Financial Portfolio Management	1.773	4	0	1.689	16	9	Financial Services
Personalized Tutor	1.765	2	1	1.916	22	11	Education
Insurance Claims Processing	1.748	3	0	1.588	12	3	Financial Services
Intelligent Customer Support Automation	1.723	6	0	1.899	20	8	Customer Service & Support
Space Exploration Data Processing	1.697	1	0	1.210	1	0	Sciences
Legal Reasoning Assistant	1.689	4	1	1.983	22	3	Legal
Construction Project Planning	1.664	1	0	1.336	2	0	Real Estate & Construction
Automated Assessment & Grading	1.655	2	0	1.479	8	3	Education
Legal Document Analysis	1.655	4	1	1.588	10	2	Legal
Legal Research Assistant	1.647	2	1	1.739	15	1	Legal
Code Generation Assistant	1.639	7	3	1.571	11	1	Software Development
Military Logistics Optimization	1.639	1	0	1.454	6	2	Defense
Automated Knowledge Base Maintenance	1.630	1	0	1.496	11	2	Knowledge Management
Military Intelligence Analysis	1.622	1	0	1.748	16	8	Defense
Property Valuation	1.597	0	0	1.370	7	0	Real Estate & Construction
Contract Analysis	1.597	2	1	1.412	9	2	Legal
Fleet Management	1.588	1	0	1.429	6	4	Transportation
Autonomous Defense Systems	1.580	0	0	1.571	15	6	Defense
Knowledge Discovery and Mining	1.580	0	0	1.529	8	2	Knowledge Management
Litigation Prediction	1.580	2	1	1.605	13	5	Legal
Automated Email Triage and Response	1.580	1	0	1.697	13	3	Customer Service & Support
Military Training Simulation	1.580	0	0	1.571	11	3	Defense
Diagnostic Support System	1.571	1	1	1.571	13	4	Healthcare
Public Transportation Optimization	1.571	0	0	1.277	6	0	Transportation
Curriculum Design	1.563	0	0	1.504	6	0	Education
Cybersecurity Threat Detection	1.563	3	0	1.353	5	3	Technology
API Integration Assistant	1.563	3	0	1.420	6	1	Software Development
Healthcare Resource Optimization	1.555	0	0	1.471	8	3	Healthcare
Drug Safety Monitoring	1.555	0	0	1.412	9	3	Pharmaceutical
Expertise Location System	1.555	0	0	1.395	8	0	Knowledge Management
Smart Grid Management	1.546	0	0	1.353	6	4	Utilities
Internal Knowledge Base Search	1.546	0	0	1.336	4	1	Knowledge Management
Population Health Management	1.538	0	0	1.580	11	5	Healthcare
Particle Physics Data Analysis	1.529	1	0	1.143	1	0	Sciences
Code Refactoring Assistant	1.529	1	0	1.185	2	0	Software Development
AI-Powered Recruitment	1.521	0	0	1.513	10	5	Human Resources
Building Performance Analysis	1.521	1	0	1.202	3	0	Real Estate & Construction
Insurance Policy Pricing Optimization	1.504	0	0	1.412	9	2	Financial Services
Energy Grid Optimization	1.487	0	0	1.218	5	2	Utilities
Smart Building Management	1.487	0	0	1.294	5	1	Real Estate & Construction
Social Media Campaign Analysis	1.487	0	0	1.529	7	1	Advertising & Marketing
Clinical Trial Optimization	1.479	0	0	1.412	7	3	Pharmaceutical
Traffic Management	1.479	0	0	1.235	6	1	Transportation
Employee Engagement Analysis	1.471	0	0	1.513	13	3	Human Resources
Bug Detection and Fixing	1.471	3	1	1.118	1	1	Software Development
Supply Chain Optimization	1.471	1	0	1.168	2	0	Logistics
Test Case Generation	1.462	1	0	1.084	0	0	Software Development
Workforce Planning and Analytics	1.462	2	0	1.420	7	2	Human Resources
Automated Code Review	1.462	1	0	1.126	1	0	Software Development
Warehouse Automation	1.454	0	0	1.244	5	1	Logistics
Database Query Optimizer	1.445	1	0	1.176	4	1	Software Development
Genomic Research Analysis	1.445	1	0	1.303	6	2	Sciences
Climate Modeling	1.445	0	0	1.193	4	0	Sciences
Precision Farming	1.445	0	0	1.176	1	0	Agriculture
Insurance Claims Fraud Detection	1.437	0	0	1.319	8	2	Financial Services
Drug Discovery Acceleration	1.437	1	0	1.311	5	0	Pharmaceutical
Design Trend Analysis	1.437	1	0	1.378	4	0	Design & Creative Services
Patient Risk Prediction	1.429	0	0	1.420	8	4	Healthcare
Customer Feedback Analysis	1.429	0	0	1.387	8	1	Customer Service & Support
Code Documentation Generator	1.420	2	0	1.151	1	0	Software Development
Production Process Optimization	1.420	0	0	1.168	2	0	Manufacturing
Assembly Line Optimization	1.412	1	0	1.185	4	0	Manufacturing
Programmatic Advertising Optimization	1.412	1	0	1.479	9	4	Advertising & Marketing
Employee Performance Analytics	1.403	0	0	1.412	9	4	Human Resources
Autonomous Vehicle Control	1.395	0	0	1.218	4	2	Transportation
Infrastructure Maintenance Prediction	1.387	0	0	1.202	2	0	Utilities
Audience Analytics and Insights	1.387	0	0	1.504	9	2	Media & Entertainment
Livestock Health Monitoring	1.387	0	0	1.227	2	0	Agriculture
Manufacturing Quality Control	1.387	0	0	1.244	1	1	Pharmaceutical
Content Recommendation Engine	1.378	0	0	1.790	17	8	Media & Entertainment
Agricultural Yield Optimization	1.378	0	0	1.193	1	0	Agriculture
Route Optimization	1.378	0	0	1.126	2	1	Logistics
Asset Management and Organization	1.370	1	0	1.193	3	0	Design & Creative Services
Resume Screener	1.361	0	0	1.277	5	1	Human Resources
Customer Segmentation and Targeting	1.353	0	0	1.479	9	5	Advertising & Marketing
Design Quality Assurance	1.345	2	0	1.160	1	0	Design & Creative Services
Marketing Attribution Modeling	1.345	2	0	1.218	2	1	Advertising & Marketing
Automated Design Generation	1.345	0	0	1.412	6	0	Design & Creative Services
Fraud Detection in Financial Transactions	1.336	0	0	1.303	6	2	Financial Services
Personalized Product Recommendations	1.336	0	0	1.336	4	3	Technology
Credit Risk Predictor	1.336	1	0	1.286	6	2	Financial Services
Usage Pattern Analysis	1.319	1	0	1.235	5	2	Utilities
Workflow Attrition Estimator	1.311	0	0	1.269	4	3	Human Resources
Student Performance Prediction	1.294	0	0	1.286	5	2	Education
Inventory Demand Forecasting	1.277	0	0	1.101	0	0	Logistics
Medical Image Analysis	1.218	1	0	1.286	6	2	Healthcare
Predictive Maintenance	1.210	0	0	1.076	1	0	Manufacturing
Automated Video Editing	1.168	0	0	1.193	2	1	Media & Entertainment
Automated Quality Testing	1.143	1	0	1.042	0	0	Manufacturing
Crop Disease Detection	1.109	0	0	1.042	0	0	Agriculture
Quality Control in Manufacturing	1.092	0	0	1.042	0	0	Manufacturing

4.2.3.2 Relevance Scores by Metric

We can also look at the relevance scores by metric; some metrics are more relevant to more use cases than others. This information is most important for model evaluators. Which metrics should they focus on? One answer is the metrics with the highest average relevance score, or the highest number of high relevance use cases, indicating that many specific applications could rely on the metric for model choice.

Note that this analysis is particularly sensitive to the kinds of use cases that are included. For instance, we have more software engineering use cases than other industries, so it is not surprising that benchmarks relevant to software engineering are more relevant to our use cases than other industries. Similarly, the safety metric “AILuminate (Indiscriminate Weapons)” is the least relevant capability metric in our analysis. This isn’t because the metric is not, in principle, relevant to some use cases, but rather it is not relevant to the group of use cases we have currently evaluated.

As we expand the number of use cases and coverage over industries, we will better be able to understand which metrics are most important for individual industries.

Capability Metric Relevance

	Average Relevance: Capability	# High Relevance: Capability	# Extreme Relevance: Capability
Agentic Tool Use (Enterprise)	3.000	18	0
LiveBench (Average)	2.874	0	0
ArenaHard (GPT-4-1106)	2.779	1	0
FailSafeQA (Robustness)	2.779	3	0
MMLU-Pro (EM)	2.621	1	0
MMMU	2.611	2	0
Big-Bench Hard (3-shot Acc.)	2.568	0	0
FailSafeQA (Context Grounding)	2.547	2	0
MuSR (Acc.)	2.537	2	0
IF-Eval (Prompt Strict)	2.537	6	0
tau-bench (airline)	2.537	2	0
DROP (3-shot F1)	2.411	0	0
Humanity's Last Exam	2.389	0	0
LiveBench (Reasoning)	2.368	0	0
LiveBench (Data Analysis)	2.337	2	0
EnigmaEval	2.253	1	0
Median Tokens/s	2.242	5	0
MMLU (Pass@1)	2.221	0	0
GPQA-Diamond (Pass@1)	2.211	0	0
FailSafeQA (Compliance)	2.147	2	0
MultiChallenge	2.147	3	2
Vista	2.105	4	0
Agentic Tool Use (Chat)	2.095	0	0
MMLU-Redux (EM)	2.095	0	0
LiveBench (Instruction Following)	2.032	0	0
SimpleBench	1.989	0	0
AlpacaEval2.0 (LC-winrate)	1.989	1	0
LiveBench (Language)	1.947	0	0
AIR-Bench-Integrity	1.926	1	0
SimpleQA (Correct)	1.874	0	0
tau-bench (retail)	1.842	2	0
Chatbot Arena Vision	1.695	0	0
SciCode	1.674	4	0
LiveCodeBench (Pass@1-COT)	1.589	3	1
C-Eval (EM)	1.526	0	0
AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	1.516	0	0
SWE Verified (Resolved)	1.505	7	2
MATH-500 (Pass@1)	1.495	0	0
AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	1.474	0	0
MASK	1.474	0	0
GPQA (0-shot Acc.)	1.453	0	0
AIR-Bench-AutomatedDecisionMaking	1.421	0	0
Chatbot Arena Coding	1.421	1	0
AIME 2024 (Pass@1)	1.411	0	0
Codeforces (Rating)	1.411	0	0
FRAMES (Acc.)	1.379	3	0
Chatbot Arena (Win Rate)	1.379	0	0
AIR-Bench-Availability	1.368	0	0
CLUEWSC (EM)	1.368	0	0
LiveBench (Coding)	1.347	1	0
HumanEval (Pass@1)	1.347	1	0
Codeforces (Percentile)	1.326	0	0
AIME 2025 II (Pass@1)	1.316	0	0
AIR-Bench-Confidentiality	1.305	0	0
CorpFin	1.274	1	0
AIR-Bench-Misinformation	1.274	0	0
MortgageTax	1.263	0	0
AIR-Bench-AdviceInHeavilyRegulatedIndustries	1.242	1	0
AIR-Bench-AutonomousUnsafeOperationOfSystems	1.221	0	0
AIME 2025 I (Pass@1)	1.221	0	0
LegalBench	1.221	6	2
AIR-Bench-Fraud	1.211	1	0
Blended Price (USD/1M Tokens)	1.211	0	0
AILuminate (Privacy)	1.211	0	0
AIR-Bench-PerpetuatingHarmfulBeliefs	1.211	2	0
Aider-Polyglot (Acc.)	1.200	1	1
LiveBench (Math)	1.200	0	0
FinanceAgent	1.200	1	0
AILuminate (Specialized Advice)	1.200	0	0
MATH Level 5 (4-shot EM)	1.189	0	0
C-SimpleQA (Correct)	1.189	0	0
TaxEval	1.189	1	1
ContractLaw	1.179	2	2
AIME (Combined)	1.179	0	0
AIR-Bench-Misrepresentation	1.168	0	0
CaseLaw	1.168	4	1
MedQA	1.147	1	1
CNMO 2024 (Pass@1)	1.147	0	0
AIR-Bench-OffensiveLanguage	1.147	0	0
AIR-Bench-ViolatingSpecificTypesOfRights	1.147	0	0
AIR-Bench-HateSpeech	1.137	2	0
AILuminate (Overall)	1.126	0	0
AIR-Bench-FraudulentSchemes	1.126	0	0
AILuminate (Hate)	1.116	0	0
AIR-Bench-CelebratingSuffering	1.105	2	0
AILuminate (Defamation)	1.105	0	0
AIR-Bench-Harassment	1.105	1	0
AIR-Bench-SupportingMaliciousOrganizedGroups	1.095	1	0
AIR-Bench-TypesOfDefamation	1.084	0	0
AIR-Bench-SowingDivision	1.084	1	0
AIR-Bench-UnfairMarketPractices	1.074	0	0
AIR-Bench-ViolentActs	1.063	0	0
AIR-Bench-SuicidalAndNonSuicidalSelfInjury	1.053	0	0
AILuminate (Non-violent Crimes)	1.053	0	0
AILuminate (Intellectual Property)	1.042	0	0
AILuminate (Child Sexual Exploitation)	1.042	0	0
AIR-Bench-ChildSexualAbuse	1.042	0	0
AIR-Bench-IllegalServicesExploitation	1.042	0	0
AIR-Bench-EndangermentHarmOrAbuseOfChildren	1.042	0	0
AIR-Bench-Monetized	1.042	0	0
AIR-Bench-AdultContent	1.042	0	0
AIR-Bench-Erotic	1.032	0	0
AIR-Bench-DepictingViolence	1.032	0	0
AIR-Bench-MilitaryAndWarfare	1.032	0	0
AILuminate (Sexual Content)	1.032	0	0
AIR-Bench-NonConsensualNudity	1.021	0	0
AIR-Bench-HighRiskFinancialActivities	1.021	0	0
AIR-Bench-IllegalRegulatedSubstances	1.021	0	0
AIR-Bench-AcademicDishonesty	1.021	0	0
AIR-Bench-PoliticalPersuasion	1.021	0	0
AIR-Bench-WeaponUsageAndDevelopment	1.021	0	0
AILuminate (Suicide & Self-Harm)	1.021	0	0
AIR-Bench-DisruptingSocialOrder	1.021	0	0
AIR-Bench-DisempoweringWorkers	1.021	0	0
AIR-Bench-DeterringDemocraticParticipation	1.021	0	0
AILuminate (Sex-Related Crimes)	1.021	0	0
AIR-Bench-OtherUnlawfulCriminalActivities	1.011	0	0
AIR-Bench-InfluencingPolitics	1.011	0	0
AILuminate (Indiscriminate Weapons)	1.000	0	0

Safety Metric Relevance

	Average Relevance: Safety	# High Relevance: Safety	# Extreme Relevance: Safety
AIR-Bench-UnauthorizedPrivacyViolations/SensitiveData	3.832	66	41
AIR-Bench-Integrity	3.747	70	18
AIR-Bench-DiscriminatoryActivities/ProtectedCharacteristics	3.347	55	30
AILuminate (Privacy)	3.326	50	29
AIR-Bench-Confidentiality	2.821	29	5
AIR-Bench-Availability	2.800	29	1
FailSafeQA (Context Grounding)	2.758	14	2
FailSafeQA (Compliance)	2.389	11	1
AIR-Bench-PerpetuatingHarmfulBeliefs	2.389	23	8
AILuminate (Hate)	2.379	22	10
MASK	2.358	18	4
AIR-Bench-OffensiveLanguage	2.168	17	3
AIR-Bench-Misrepresentation	2.126	19	1
AIR-Bench-Misinformation	2.084	18	3
AILuminate (Overall)	2.074	15	3
AIR-Bench-ViolatingSpecificTypesOfRights	1.937	13	1
AIR-Bench-AutomatedDecisionMaking	1.863	14	1
AIR-Bench-TypesOfDefamation	1.853	11	1
AILuminate (Intellectual Property)	1.821	10	1
AILuminate (Defamation)	1.800	10	4
AIR-Bench-HateSpeech	1.800	8	4
AIR-Bench-Fraud	1.789	13	0
AILuminate (Specialized Advice)	1.768	15	6
AIR-Bench-AutonomousUnsafeOperationOfSystems	1.726	14	5
AIR-Bench-Harassment	1.695	7	4
FailSafeQA (Robustness)	1.674	0	0
AIR-Bench-AdviceInHeavilyRegulatedIndustries	1.663	16	4
AIR-Bench-SupportingMaliciousOrganizedGroups	1.663	7	5
AIR-Bench-SowingDivision	1.653	6	3
AIR-Bench-DisempoweringWorkers	1.600	6	0
AIR-Bench-UnfairMarketPractices	1.589	8	2
AIR-Bench-CelebratingSuffering	1.526	7	2
AIR-Bench-OtherUnlawfulCriminalActivities	1.495	4	1
AIR-Bench-FraudulentSchemes	1.400	3	0
AIR-Bench-ViolentActs	1.389	4	2
AILuminate (Non-violent Crimes)	1.379	3	0
Chatbot Arena (Win Rate)	1.358	0	0
IF-Eval (Prompt Strict)	1.316	0	0
AIR-Bench-WeaponUsageAndDevelopment	1.305	5	1
AIR-Bench-SuicidalAndNonSuicidalSelfInjury	1.295	5	2
AILuminate (Suicide & Self-Harm)	1.295	4	2
MedQA	1.295	3	2
AIR-Bench-ChildSexualAbuse	1.284	4	2
tau-bench (retail)	1.284	0	0
AILuminate (Sexual Content)	1.284	3	1
AIR-Bench-PoliticalPersuasion	1.263	3	0
AIR-Bench-AdultContent	1.253	4	0
AILuminate (Indiscriminate Weapons)	1.242	3	2
AIR-Bench-IllegalServicesExploitation	1.232	3	1
AILuminate (Child Sexual Exploitation)	1.232	4	3
AIR-Bench-IllegalRegulatedSubstances	1.211	2	0
AIR-Bench-EndangermentHarmOrAbuseOfChildren	1.211	3	3
AIR-Bench-DepictingViolence	1.200	2	0
AIR-Bench-MilitaryAndWarfare	1.200	4	3
AIR-Bench-InfluencingPolitics	1.200	1	0
AIR-Bench-AcademicDishonesty	1.158	2	2
AILuminate (Sex-Related Crimes)	1.147	2	0
MultiChallenge	1.137	0	0
AIR-Bench-Erotic	1.137	1	0
LiveBench (Instruction Following)	1.126	0	0
AIR-Bench-NonConsensualNudity	1.126	2	2
Humanity's Last Exam	1.116	0	0
ArenaHard (GPT-4-1106)	1.105	0	0
AIR-Bench-Monetized	1.105	1	0
AIR-Bench-DeterringDemocraticParticipation	1.105	1	0
Vista	1.095	0	0
AIR-Bench-HighRiskFinancialActivities	1.095	1	0
LegalBench	1.084	0	0
Agentic Tool Use (Enterprise)	1.074	0	0
MMMU	1.074	0	0
tau-bench (airline)	1.063	0	0
LiveBench (Average)	1.063	0	0
AIR-Bench-DisruptingSocialOrder	1.063	1	0
ContractLaw	1.053	0	0
CaseLaw	1.042	0	0
TaxEval	1.032	0	0
LiveBench (Language)	1.021	0	0
CorpFin	1.021	0	0
SimpleBench	1.021	0	0
Agentic Tool Use (Chat)	1.021	0	0
LiveBench (Reasoning)	1.011	0	0
CLUEWSC (EM)	1.011	0	0
LiveCodeBench (Pass@1-COT)	1.011	0	0
AlpacaEval2.0 (LC-winrate)	1.011	0	0
C-SimpleQA (Correct)	1.011	0	0
Chatbot Arena Vision	1.011	0	0
FinanceAgent	1.011	0	0
EnigmaEval	1.000	0	0
LiveBench (Data Analysis)	1.000	0	0
Median Tokens/s	1.000	0	0
MMLU (Pass@1)	1.000	0	0
GPQA-Diamond (Pass@1)	1.000	0	0
SimpleQA (Correct)	1.000	0	0
MMLU-Redux (EM)	1.000	0	0
DROP (3-shot F1)	1.000	0	0
MuSR (Acc.)	1.000	0	0
Big-Bench Hard (3-shot Acc.)	1.000	0	0
MMLU-Pro (EM)	1.000	0	0
MATH Level 5 (4-shot EM)	1.000	0	0
SWE Verified (Resolved)	1.000	0	0
SciCode	1.000	0	0
Codeforces (Percentile)	1.000	0	0
AIME (Combined)	1.000	0	0
LiveBench (Math)	1.000	0	0
CNMO 2024 (Pass@1)	1.000	0	0
Aider-Polyglot (Acc.)	1.000	0	0
Blended Price (USD/1M Tokens)	1.000	0	0
MortgageTax	1.000	0	0
AIME 2025 II (Pass@1)	1.000	0	0
HumanEval (Pass@1)	1.000	0	0
C-Eval (EM)	1.000	0	0
LiveBench (Coding)	1.000	0	0
FRAMES (Acc.)	1.000	0	0
Codeforces (Rating)	1.000	0	0
AIME 2024 (Pass@1)	1.000	0	0
Chatbot Arena Coding	1.000	0	0
GPQA (0-shot Acc.)	1.000	0	0
MATH-500 (Pass@1)	1.000	0	0
AIME 2025 I (Pass@1)	1.000	0	0

4.2.4 Context-adapted Model Scoring

With our relevance scoring system established, we can now examine how specific models perform in real-world contexts.

4.2.4.1 Example Model Evaluations: Claude 3.5 Sonnet for Content Moderation

Let’s see how these scores help to evaluate a single model for a particular use case, continuing with the Content Moderation example. We’ll look at the top 20 metrics for Claude 3.5 Sonnet and how weighting them by their relevance impacts their contribution to capability and safety scores.

Below are the top 10 normalized evaluations we collected for Claude 3.5 Sonnet, along with the relevance scores for each metric/dimension and the combined weight by dimension

Capability and Safety Scores

As we mentioned, context-adapted evidence strength differs from the generic case in that it is now a function of the evidence relevance rather than the number of evaluations. Below you can see how different evaluation scores are weighted by their capability and safety relevance.

	Score	Capability Relevance	Capability Weighted Score	Safety Weighted Score	Safety Relevance	Description
AIR-Bench-Fraud	1.000	4.0	4.000	4.000	4.0	AIR-Bench-Fraud: A measure of model refusal for Deception (Level-1: Societal Risks, Level-2: Deception) related to fraud. Includes Level-4 risks like spam, scams, phishing/catfishing, pseudo-pharmaceuticals, and impersonating others.
AIR-Bench-HateSpeech	0.996	4.0	3.984	4.980	5.0	AIR-Bench-HateSpeech: A measure of model refusal for Hate/Toxicity (Level-1: Content Safety Risks, Level-2: Hate/Toxicity) related to hate speech. Includes Level-4 risks targeting various characteristics like race, ethnicity, color, gender, sexual orientation, religion, beliefs, nationality, geographic region, caste, social behaviors, physical characteristics, mental characteristics, personality, health conditions, disability, pregnancy status, genetic information, occupation, and age.
AIR-Bench-SupportingMaliciousOrganizedGroups	0.989	4.0	3.956	4.945	5.0	AIR-Bench-SupportingMaliciousOrganizedGroups: A measure of model refusal for Violence & Extremism (Level-1: Content Safety Risks, Level-2: Violence & Extremism) related to supporting malicious organized groups. Includes Level-4 risks like extremism, terrorism, and criminal organization support.
AIR-Bench-PerpetuatingHarmfulBeliefs	0.952	4.0	3.808	4.760	5.0	AIR-Bench-PerpetuatingHarmfulBeliefs: A measure of model refusal for Hate/Toxicity (Level-1: Content Safety Risks, Level-2: Hate/Toxicity) related to perpetuating harmful beliefs. Includes Level-4 risks like negative stereotyping of any group, perpetuating racism, and perpetuating sexism.
AIR-Bench-CelebratingSuffering	0.862	4.0	3.448	4.310	5.0	AIR-Bench-CelebratingSuffering: A measure of model refusal for Violence & Extremism (Level-1: Content Safety Risks, Level-2: Violence & Extremism) related to celebrating suffering. Includes Level-4 risks like glorifying violence, abuse, or suffering of others; belittling victimhood or violent events; denying well-documented major violent events; and beautifying/whitewashing acts of war or aggression.
AIR-Bench-TypesOfDefamation	1.000	3.0	3.000	5.000	5.0	AIR-Bench-TypesOfDefamation: A measure of model refusal for Defamation (Level-1: Societal Risks, Level-2: Defamation) related to types of defamation. Includes Level-4 risks like disparagement, libel, and slander.
AIR-Bench-SowingDivision	1.000	3.0	3.000	5.000	5.0	AIR-Bench-SowingDivision: A measure of model refusal for Manipulation (Level-1: Societal Risks, Level-2: Manipulation) related to sowing division. Includes Level-4 risks like inducing internal conflict and deflecting scrutiny from harmful actions.
AIR-Bench-ChildSexualAbuse	1.000	3.0	3.000	5.000	5.0	AIR-Bench-ChildSexualAbuse: A measure of model refusal for Child Harm (Level-1: Content Safety Risks, Level-2: Child Harm) related to child sexual abuse. Includes Level-4 risks like solicitation and child sexual abuse material (CSAM).
AIR-Bench-ViolatingSpecificTypesOfRights	1.000	3.0	3.000	4.000	4.0	AIR-Bench-ViolatingSpecificTypesOfRights: A measure of model refusal for Fundamental Rights (Level-1: Legal and Rights-Related Risks, Level-2: Fundamental Rights) related to violating specific types of rights. Includes Level-4 risks like IP rights/trade secrets, likeness rights, reputational rights, honor, and name rights violations.
AIR-Bench-SuicidalAndNonSuicidalSelfInjury	1.000	3.0	3.000	5.000	5.0	AIR-Bench-SuicidalAndNonSuicidalSelfInjury: A measure of model refusal for Self-harm (Level-1: Content Safety Risks, Level-2: Self-harm) related to suicidal and non-suicidal self-injury. Includes Level-4 risks like suicide, cutting, and eating disorders (anorexia/bulimia).

Overall Score and Operational Metrics

If you took the average of the capability weighted score and safety weighted score above you’d end up with a final capability and safety score. This calculation is an illustration of our method but isn’t exactly how we calculate the scores. We first transform the likert scale as described in our methods and combine with the prior.

To get the overall score we take the geometric mean of the capability and safety score, along with the affordability and speed scores.

4.2.5 Aggregation and Analysis: Summarizing Model Trust Scores

Now that we have gone through a single example, we have now arrived at the final step of the Model Trust Score Framework - model and use-case wide analysis. We can now take a model and a use case and get a final score for the model’s suitability for the use case. By doing this for all models and use cases, we can get a comprehensive view of the model landscape.

4.2.5.1 Single Dimension Industry Analysis

The simplest way to view this information is to summarize by industry. By taking the average across use cases for each industry we get an overall score for each model for that industry.

It’s clear that, based on benchmarks, different models should be differentially selected for different industries. OpenAI’s o3 mini is currently the top model for financial services according to our “overall” score, but falls short of Claude-3.5 Sonnet for legal use cases (in this case, due to poor performance on legal benchmarks according to val.ai). While we don’t see huge differences between model capabilities, we see larger differences in safety scores. This is due to the evaluation data itself - most models do not have public safety benchmarks available and thus models that do (and do well, like Claude 3.5 Sonnet) perform very well.

However, some models do seem to perform better more often across industries. Reasoning models stand above others, with OpenAI’s o1 and o3, DeepSeek’s R1, and Claude-3.7 all showing high capabilities. Some industries are also better served by the current crop of models - Legal, Software Development and Technology all have higher capability scores across the board. While this latter fact may be due to the model’s genuinely performing better for specific industries, our results are also a function of the uneven coverage of industries by different benchmarks (see Relevance Scores by Industry & Use Case for more). Benchmarks have been developed that are specific for legal use cases (e.g., LegalBench) and software engineering (e.g., Software Engineering Benchmarks), which affords more confident statements about model capabilities and safety.

Below we visualize the average score for each industry and each model. The models are sorted by their average score on the chosen dimension.

4.2.5.2 Multi-Dimensional Industry Analysis

While the single dimensional approach approach is helpful, we also care about tradeoffs, which require addressing multiple dimensions at the same time. For instance, many models perform similarly, but some are quite a bit cheaper.

In the below visualization two dimensions can be plotted against each other to understand the tradeoffs involved in model selection.

In addition, We can also average the scores for each model across all use cases, giving a generalized enterprise suitability score along different dimensions. This is not the same as the generic evaluation, as the totality of our use cases are not “generic”. They still relate to uses that are relevant for different enterprises. So one can think of this aggregation step as a way to get a holistic understanding of a model’s capabilities and safety across a wide range of use cases within the enterprise context.

We can then view how this “average” enterprise performance compares to specific industries. “Financial Industries” is selected by default.

4.2.5.3 How to use Model Trust Scores

How can scores be used for decision making? There are a number of ways, but they all are fundamentally based in evaluating model tradeoffs. While it may occasionally be the case that one model is more capable and safer than all others (capability and safety don’t necessarily trade off!), it’s unlikely that the same model will also be the cheapest, or fastest.

One starting point is looking at the “Overall Score” against “Cost”. This showcases a balanced measure of capability and safety against cost. It may be helpful to restrict the range of the x-axis, because o1 costs far more than the rest, obscuring the tradeoffs amongst the cost competivive models.

If safety concerns are not as critical for the use case, “Capability” vs “Cost” may be more relevant. If we look at this comparison, it becomes obvious why DeepSeek R1 made a splash in the AI ecosystem. Beyond being a new player in the AI ecosystem from China, DeepSeek R1 is genuinely high performing and very inexpensive compared to its peers, as can be seen below (note that the X-axis range has been restricted which removes o1 from the plot. o1 is significantly more expensive than the rest of the models).

4.2.5.4 Model Ranking, AI Systems, and Caveats

Clearly, this tool can potentially enable making very powerful statements about the capability and safety of the models in the AI ecosystem. However, we do not have access to the models’ “true” capabilities and safety. We can only make statements about models based on the benchmarks we have available. For instance, we do not have AILuminate scores for DeepSeek R1 and thus can’t make highly informed statements about its safety. We deal with this by appealing to a pessimistic prior, but this reflects a precautionary principle - not an accurate estimate of the model’s true safety.

Model Trust Scores provides an informed and actionable synthesis of existing evaluations, but requires a robust evaluation ecosystem to be most useful.

We believe the onus should be on model providers to demonstrate their model’s safety and capabilities in a way that is transparent and applicable to the applications they propose. This means running internal evaluations and seeking out independent third-parties to evaluate their models. The Model Trust Score pessimistic prior essentially downgrades models that are not evaluated well by the ecosystem. We believe this is a reasonable compromise and connects to responsible decision making within enterprises. As powerful AI capabilities become increasingly accessible, why trust a model that hasn’t proven its trustworthiness? We believe that Model Trust Scores can help galvanize a more comprehensive ecosystem evaluations by showcasing industry gaps in evaluation coverage and downgrading models that fall below evaluation expectations due to poor performance or poor transparency.

Moreover, we are synthesizing benchmarks on AI models, not AI systems tuned for a particular use case (or benchmark). It is likely that every model can do better on certain benchmarks with the proper scaffolding, just as an AI model within a particular use case application will do much better than a naive evaluation of the model would imply. The approach we take can easily generalize to AI systems however. As long as there is an evaluation, we can identify its use case relevance and make context-specific claims of an AI system’s suitability, whether a base model, tool-using agent, or any other system.

5 Conclusion

5.1 Future Work: Improving the Evaluation Landscape and Certification

Our analysis of relevance scores reveals a critical insight: many industries lack benchmarks that directly measure the capabilities needed for their specific use cases. While Model Trust Scores help organizations make the best decisions possible with current evaluations, there’s significant room for improvement in how we assess models for enterprise use.

5.1.1 Developing Use Case Specific Evaluations

The path forward requires developing benchmarks that more precisely target individual industries and use cases. Our relevance scoring system not only helps contextualize existing benchmarks but also highlights which industries are most underserved by current evaluation approaches. This information proves particularly valuable when combined with risk assessments – industries that are both underserved by benchmarks and face significant potential harms from AI deployment should be prioritized for evaluation development.

These improved evaluations can emerge from several sources. It is an active area of research and institution development to figure out how to best do this: - Third-parties (e.g., industry consortiums, nonprofits) creating benchmarks and standardized test suites (AILuminate by MLCommons is a good example here) - Research institutions exploring novel assessment methods (LegalBench is an industry specific evaluation developed by an open scientific effort led by Stanford University) - AI providers and deployers could develop and share evaluation approaches relevant for real-world applications (e.g., SimpleQA from OpenAI or Model Written Evals by Anthropic) - Regulatory bodies establishing compliance frameworks founded on quantitative evaluation - AI Safety Institutes, either alone or in partnership with other organizations mentioned above.

As evaluations mature for specific use cases, we can move beyond individual evaluations toward comprehensive assessment frameworks. When we can reliably measure all relevant dimensions of a use case – from technical capabilities to safety controls – we can develop omnibus scores that simplify model selection while maintaining rigor.

5.1.2 From Relative to Absolute Trust

Model Trust Scores currently helps organizations compare models relative to one another, identifying which options are safer or more capable within the available choices. This relative assessment provides crucial guidance for model selection. However, the future of AI governance requires moving beyond relative comparisons to “absolute trust”, potentially reflected by third-party certifications, where the assessment results are placed in the context of best practice and thorough and independent risk/benefit analyses.

An assessment framework for certifications would answer fundamental questions: - Does any available model meet the minimum capability requirements for this use case? - Are there safety thresholds below which no model should be deployed, regardless of capabilities? - What level of evidence is required to establish trustworthiness in high-stakes contexts?

Our current safety and capability scores provide comparative insights but don’t yet map directly to real-world suitability thresholds. Establishing these thresholds – particularly when they must account for multiple dimensions of performance and risk – represents a crucial step toward meaningful AI certification frameworks which can further bolster ecosystem trust and information sharing.

This evolution from relative comparison to certification would transform how organizations approach AI adoption. Rather than simply choosing the best available option, they could confidently determine whether any current model meets their requirements. This shift becomes especially critical as AI systems take on increasingly consequential roles across industries. Whether these certifications are mandated for use (as in the case of permits) or informative to market actors (as in the case of third-party labeling) is a downstream question beyond the scope of this paper.

The path to certification requires collaboration between multiple stakeholders: - Industry experts who understand use case requirements - Safety researchers who can establish risk thresholds - Evaluation specialists who can design comprehensive tests - Regulatory bodies who can standardize certification processes - Enterprise users who can validate real-world performance

As we develop these more sophisticated evaluation and certification frameworks, the Model Trust Score Framework will evolve to incorporate both relative and absolute assessments, providing organizations with increasingly comprehensive guidance for safe and effective AI adoption.

5.2 Bridging the Gap Between Governance & Assurance

There is often a significant gap between governance considerations and technical evaluations—how do you know whether a particular evaluation result is good or secure or compliant?

The Credo AI Platform is designed to bridge this gap. Through Model Trust Scores, the platform ingests structured model-level benchmarks from academic and public sources, providing organizations with the tools to interpret these results in a governance and risk context.

But evaluating models isn’t just about interpreting existing benchmarks—it’s also about determining what additional assessments are needed for a specific use case. Credo AI helps governance teams define these requirements, guiding implementers on what additional evaluations to run based on risk thresholds, regulatory obligations, and enterprise policies. Benchmarks are helpful for a first pass, but context-specific assessments are critical to making risk-informed decisions about which models to trust in critical business applications.

By translating governance decisions into technical configurations and automating policy-to-code workflows, Credo AI ensures that evaluation insights drive real enforcement. Tight integrations with ops providers make it easy to run necessary evaluations and pull results back into the platform, where they become part of a unified governance repository. This structured, closed-loop approach empowers organizations to visualize, understand, and act on AI risks—establishing Credo AI as the single source of truth for AI governance.