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Start with the system’s real-world use
An AI system is more than a model. Its inputs, interfaces, human decisions, operating conditions, and downstream effects all shape what it does to people. A model that performs acceptably in one context may create different risks when used by different people, for a different purpose, or with different consequences.
Before testing, describe the system’s intended purpose, who will use it, who may be affected, where and under what conditions it will operate, and what decisions it may influence. Include foreseeable misuse and the consequences of a wrong answer, an unavailable system, or a person being unable to challenge an outcome. This context determines which groups, risks, and tests matter.
NIST’s AI RMF 1.0, released January 26, 2023, treats trustworthiness as a set of characteristics that must be considered together. NIST says the framework is voluntary, not a universal legal requirement; as of October 7, 2026, its overview says the framework is being revised. It does not establish one fairness threshold for every system.
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Use a repeatable evaluation workflow
1. Govern: assign responsibility
Name the people accountable for evaluation, approval, and monitoring. Include relevant technical, domain, privacy, accessibility, and community perspectives; a team that understands the model but not the people affected by it may miss important harms.
- Specify what evidence is required before use and who decides whether it is sufficient.
- Identify who can pause or stop deployment, how concerns are escalated, and who approves residual risk.
- Assign an owner for post-deployment monitoring and for reviewing material system changes.
2. Map: define context and potential harm
Record the system’s purpose, users, affected people, operating environment, dependencies, and foreseeable misuse. Identify the decisions it informs, whether a human can override or challenge them, and how people can seek correction or recourse. Consider potential benefits alongside harms, including disparate effects, privacy intrusion, and cases where people may not know they are interacting with AI.
3. Measure: test the risks that matter
Test under conditions that reflect the intended use, not only on an overall benchmark. Use the same task, inputs, user roles, and operating assumptions when comparing systems. Record what was tested, what was not, and the limitations of the evidence.
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4. Manage: decide, document, and revisit
For each material risk, record the evidence, severity, affected groups, planned mitigation, responsible owner, and remaining risk. Decide whether to proceed, restrict the use, or reject it. Set monitoring triggers and review the decision when data, models, users, or operating conditions change.
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NIST’s AI RMF Playbook provides suggested actions and documentation practices for the four functions. It is based on AI RMF 1.0 and is expected to be updated after the framework revision. NIST’s TEVV-Athlon announcement described an initial public draft for adaptable assessment across statistical machine learning, large language models, multimodal models, and agentic systems, with feedback requested through October 6, 2026. That announcement describes a draft, not a settled standard; its status after the stated feedback window is not established here.
Assess bias and fairness beyond demographic averages
Bias can enter through social and institutional systems, data and statistical methods, or human judgment and use. NIST distinguishes systemic, computational/statistical, and human-cognitive sources. A system with some bias mitigated is not automatically fair: fairness depends on the use, the people affected, and the consequences.
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Identify the groups and intersections relevant to the application, then examine the full path from data to outcome:
- Data: review provenance, coverage, representation, and whether labels or measurements reflect the intended concept.
- Model behavior: compare errors and other meaningful outcomes across relevant groups, not just aggregate accuracy or prediction rates.
- Use by people: examine how operators interpret, rely on, override, or disregard results. Human involvement does not by itself remove bias.
- Access and downstream effects: test whether people with disabilities or limited access face barriers, and whether a model output changes later decisions or opportunities.
Choose fairness criteria that fit the application and affected communities, and explain why those criteria were selected. Do not present similar aggregate rates as proof of fairness, or treat one metric as a universal pass/fail standard.
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Inventory the data the system receives and produces, who can access it, how it is shared, and how long it is retained. Include data used to develop or operate the system where relevant. Ask whether identity or sensitive attributes can be inferred from inputs or outputs—even if those attributes were not directly collected.
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Consider data minimization, de-identification, aggregation, and privacy-enhancing technologies where they fit the use case. Then test their effects in context. For example, privacy controls can affect accuracy, particularly when data is sparse, and may interact with fairness and transparency. Record those tradeoffs rather than treating privacy as a separate box to check.
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Transparency is about what information is available concerning the system and its outputs. Decide what affected people, operators, auditors, and decision-makers need to know, and provide it in a timely, understandable form. Relevant information may include the system’s purpose, capabilities and limits, data, output meaning, human roles, and who is responsible.
Keep three related ideas distinct: transparency describes what happened; explainability concerns how a result was produced; interpretability concerns why it matters and what it means in context. An explanation of one output does not replace clear information about the system’s purpose, limits, or accountability.
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Compare systems using the same conditions
When evaluating alternatives, compare them on the same intended task and use conditions. Treat the axes below as prompts for evidence and discussion, not universal thresholds.
| Evaluation area | Evidence to compare |
|---|---|
| Performance and fairness | Overall performance, error patterns across relevant groups, and downstream outcomes. |
| Accessibility | Whether people who face barriers can use the system and whether those barriers affect outcomes. |
| Privacy | Data collected or used, retention, access and sharing, inference risk, and privacy controls. |
| Transparency | What affected people and operators can learn about purpose, capabilities, limits, and outputs, and whether the information is timely and understandable. |
| Human oversight and recourse | Who can review or correct an outcome, override the system, appeal, or seek help. |
| Robustness | How behavior changes with different inputs, operating contexts, and foreseeable misuse. |
| Evidence and accountability | Test limitations, monitoring plans, named owners, and decisions about residual risk. |
Include other trustworthiness risks where relevant
Bias, privacy, and transparency sit alongside validity, reliability, safety, security, and resilience. Which characteristics deserve the most attention depends on the setting, and weaknesses can interact: a privacy control may affect performance, or an opaque process may make it harder to detect unfair outcomes. NIST cautions that addressing trustworthiness characteristics individually does not ensure that an AI system is trustworthy; tradeoffs are common, and not every characteristic applies equally in every setting.
For generative AI, NIST’s Generative AI Profile identifies additional considerations, including bias and automation bias. The latter matters when people over-rely on generated or recommended outputs. Build the evaluation around the actual product and use, rather than assuming the same checks fit every kind of AI system.
Keep the decision tied to evidence
A useful evaluation produces a decision record, not just test scores. Capture the use context, affected groups, methods and results, known limitations, chosen criteria, mitigations, tradeoffs, residual risks, and the people responsible for acting on them. Define what changes or incidents trigger a new review, and ensure the monitoring owner has authority to respond.
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NIST’s AI RMF and its TEVV-Athlon draft offer structures for organizing risk management and adaptable evaluation; neither supplies a universal legal checklist or a single pass/fail score for every application. Sector-specific duties and thresholds depend on the system, deployment location, affected population, and applicable law.
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