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Start with the decision the system will influence
A model score or product description cannot tell you by itself whether an AI system is suitable. Assess the whole sociotechnical system: the model, its data and interfaces, the people operating it, the decisions it informs, and the setting in which those decisions affect people.
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NIST’s AI Risk Management Framework (AI RMF) offers a voluntary structure for this work. Its four functions—Govern, Map, Measure, and Manage—organize risk management across a system’s lifecycle. NIST says the importance of trustworthiness characteristics varies by setting and that tradeoffs can arise; addressing each characteristic in isolation does not guarantee a trustworthy system.
Write down the use case
Before testing, record the intended purpose, users, affected people, operating conditions, and decisions the system may influence. Include dependencies such as data sources, connected services, and human review. Describe foreseeable misuse as well as normal use, and consider what happens if the system is wrong, unavailable, or used outside its intended setting.
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- What benefit is the system expected to provide, and how will you know whether it does?
- Who may be affected, including people who do not directly use the system?
- Can a person challenge or correct an output, and who can override it?
- What are the likely consequences of an incorrect, delayed, or missing output?
- Could people be affected without knowing an AI system is involved?
Govern the evaluation before testing begins
Assign named responsibility for running the assessment, approving residual risk, and monitoring the system after deployment. Involve the expertise the use case requires: for example, domain, technical, privacy, accessibility, and community perspectives. Decide in advance what evidence is required, who may pause or stop deployment, how concerns are escalated, and who owns follow-up actions.
Include affected communities where their experience can reveal risks that technical teams may miss. Record who was consulted and how their input changed the evaluation or decision. Governance is not just sign-off at the end; under the NIST AI RMF, it is a core function that supports the rest of the risk-management work.
Measure bias and fairness in context
Do not reduce bias review to checking whether a dataset has demographic balance. NIST describes systemic, computational and statistical, and human-cognitive sources of bias. Bias can enter through institutional practices, data collection, labels, measurement choices, model behavior, or how people rely on outputs. And reducing measured bias does not by itself establish that a system is fair.
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Choose relevant groups and outcomes
Identify groups and intersections relevant to the application, including people who may face accessibility or other barriers. Then decide which outcomes matter: errors, delays, denial of service, additional scrutiny, missed opportunities, or downstream effects after a human acts on an output. A system used to rank job applications, for example, needs evaluation tied to that decision and its consequences—not just a general accuracy score.
Examine the full path from data to impact
- Data: Record where data came from, whom it represents, what it leaves out, and how labels or measurements were produced.
- Model behavior: Compare error patterns and other relevant outcomes across the selected groups under realistic operating conditions. Examine intersections when the data and use case support meaningful analysis.
- Human use: Check whether operators interpret outputs consistently, can identify uncertainty, and can challenge a result. Human involvement is not automatically a safeguard if people defer to the system or lack the ability to intervene.
- Accessibility and downstream effects: Test whether people with disabilities or limited access may be disadvantaged, and trace how outputs influence later decisions or services.
Similar aggregate prediction rates are not proof of fairness. Select measures and acceptance criteria to fit the use case, explain why they are appropriate, and involve affected communities when deciding which harms and tradeoffs deserve priority. The cited NIST materials do not establish one fairness threshold for every AI system.
Review privacy across inputs, outputs, and operation
Privacy assessment should cover more than whether the model was trained on personal data. Inventory the data the system receives and produces, how long it is kept, who can access it, where it is shared, and what can be inferred from it. An output may reveal someone’s identity or private attributes even if those details were not directly collected as inputs.
Build a data-flow inventory
For each input and output, document its source, purpose, sensitivity, access controls, retention, and sharing. Include prompts, uploaded files, logs, generated results, and data sent to connected services when those are part of the system. Identify whether operators can see individual records and whether outputs are stored or used for other purposes.
Test controls and their tradeoffs
Consider whether the system can use less data, shorter retention, restricted access, de-identification, aggregation, or privacy-enhancing technologies. These controls are not interchangeable, and none should be treated as proof that re-identification or inference is impossible. Check whether a person’s identity or private attributes could be inferred from realistic inputs or outputs.
Measure how privacy controls affect performance and fairness in the intended setting. NIST notes that privacy techniques can interact with other trustworthiness characteristics; for example, some techniques may reduce accuracy when data are sparse. Record the tradeoff and decide whether the remaining performance is acceptable for the task rather than assuming that a privacy control has no operational cost.
Make transparency useful to each audience
Transparency means making relevant information about the system and its outputs available to people who need it. The right information depends on the audience: a person affected by a decision may need to know that AI was used and how to seek review, while an operator or auditor may need technical documentation and evidence about limitations.
NIST distinguishes three related ideas: transparency concerns what happened; explainability concerns how a result was produced; and interpretability concerns what the result means in context. A fluent explanation is not necessarily a faithful account of how a system arrived at an output, and an explanation alone does not establish that the result is correct or fair.
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Plan information for affected people, operators, auditors, and decision-makers. Depending on the use, relevant information may include:
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- the system’s purpose, capabilities, and limitations;
- what data it uses and what outputs mean;
- the role of human reviewers and who is responsible for decisions;
- how to correct information, challenge an output, or request human review; and
- known failure conditions and circumstances in which the system should not be used.
Test whether disclosures are timely and understandable to their intended audience. A notice that arrives only after a consequential decision may not help someone respond to it.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare systems using the same conditions
When assessing alternatives, compare them on the same intended task, data conditions, user workflow, and affected populations. A vendor’s headline benchmark or feature list is not a substitute for evidence about the deployment you are considering. Use a common set of questions and record evidence quality as well as the result.
| Comparison area | What to examine |
|---|---|
| Performance and bias | Overall performance, error patterns across relevant groups, accessibility, and downstream effects. |
| Privacy | Data collected or used, retention, access, sharing, inference risks, and privacy controls. |
| Transparency and recourse | What affected people and operators are told, whether information is understandable and timely, and how correction, appeal, or human review works. |
| Operational resilience | Behavior when inputs or context change, and exposure to foreseeable misuse or failure. |
| Evidence and ownership | Test conditions, limitations, monitoring plan, responsible owners, and who accepts residual risk. |
Do not turn these axes into a universal score unless the scoring method and weights are justified for the particular application. A system can perform well on one dimension and still be unsuitable because of a serious weakness elsewhere.
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Manage risk and revisit the decision
For each material risk, document the evidence, severity, affected groups, chosen mitigation, accountable owner, and remaining risk. Make an explicit decision to proceed, limit the use, or reject it. If a risk is accepted, record who accepted it and why; do not leave that choice implicit in a test report.
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Set monitoring triggers before deployment. Reassess when the data, model, users, workflow, or operating context changes, and when monitoring reveals unexpected errors or harms. The system’s suitability is a lifecycle question, not a one-time procurement decision.
NIST’s AI RMF Playbook provides suggested actions and documentation practices for the four functions; it is based on AI RMF 1.0. NIST’s overview reports that the framework is being revised and notes an April 7, 2026 concept note for a critical-infrastructure profile. NIST also announced a draft TEVV-Athlon assessment approach intended to be extensible across statistical machine learning, large language models, multimodal models, and agentic systems; the announcement’s feedback window ran through October 6, 2026. Treat that announcement as describing a draft resource, not a settled standard.
These resources support structured evaluation, but they do not by themselves determine jurisdiction-specific legal duties or sector-specific thresholds. Those depend on the system, deployment location, affected population, and applicable law.
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