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Test an AI tool against the task, people, and conditions in which you plan to use it—not a generic score or a polished demo. A useful evaluation measures realistic performance, checks for harmful differences between affected groups, traces what happens to submitted data, and tests whether people can use the complete interface. The result should be a documented decision about whether the tool is suitable, what safeguards it needs, and when to test it again.
Start by defining what “fit for use” means
An AI tool does not have one context-free accuracy or fairness score. Its suitability depends on its intended task, its users, the conditions in which it operates, and the consequences of a mistake. The National Institute of Standards and Technology (NIST) treats AI trustworthiness as a contextual, sociotechnical question: data, model choices, organizational decisions, and human oversight all matter. Its AI Risk Management Framework is voluntary, and its referenced Generative AI Profile was released on July 26, 2024. Check official NIST materials for the current framework status and versions before using them as a governance reference.
Before testing, write a short evaluation brief. Make decisions about the task and risks first; otherwise, it is easy to collect scores that do not answer the real question.
- Intended use: State the exact job the system will do, what it will not do, and whether it supports or makes a decision.
- People and setting: Identify who will use it, who will be affected by its outputs, and the languages, devices, workflows, and operating conditions involved.
- Inputs and outputs: Describe what information users provide and what the system returns, including formats that need to be reviewed or acted on.
- Error consequences: List plausible false alarms, missed cases, incorrect generated content, or inaccessible interactions—and who bears the cost.
- Required performance and safeguards: Set acceptable thresholds for the specific task, decide which failures require human review, and define when an output must be withheld or escalated.
- Evaluation owner: Assign someone to approve the test design, review results, record limitations, and decide whether to proceed.
There is no universal threshold that makes an AI system safe. A mistaken label in a low-consequence sorting task is not equivalent to a missed warning in a high-impact workflow. NIST says that selecting metrics and thresholds requires human judgment informed by context. Include people with subject-matter knowledge and perspectives from the groups most affected by the decision.
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Build an accuracy test that resembles real use
Accuracy testing asks whether the system produces correct or useful results for the task it is meant to perform. A clean demonstration set is not enough: the test should resemble the inputs, variation, and operating conditions expected in practice. NIST recommends clearly defined, realistic test sets representative of expected use, alongside documented methodology. The U.S. Intelligence Community (IC) AI Ethics Framework likewise emphasizes testing for known and reasonably foreseeable risks before deployment.
1. Assemble representative examples
Use examples drawn from the intended task and conditions, rather than only ideal inputs. Include the kinds of variation that matter in your setting: for example, different document layouts for an extraction task, or varied phrasing for a support-answering task. Those are test-design examples, not a guarantee that any particular sample is representative. Describe how examples were selected, what population or operating conditions they reflect, and important gaps.
Where feasible, keep evaluation examples separate from material used to configure or tune the system. If the system or its surrounding workflow has already seen the answers, performance on those same cases may not show how it will perform on new cases. For every test item, establish an expected answer, acceptable range, or reviewable outcome before scoring the system. If reasonable experts could disagree about the correct answer, specify how that disagreement will be handled instead of treating an ambiguous label as ground truth.
2. Choose measures that match the task and the cost of errors
Do not force every AI tool into one metric. Select measures that reflect the task and distinguish the errors that matter most.
| Task or question | Useful measure or review | What to watch for |
|---|---|---|
| Classification or retrieval | Precision and recall | Precision concerns how often returned positive results are correct; recall concerns how many relevant positive cases are found. Which matters more depends on the cost of false alarms versus missed cases. |
| Different consequences for different error types | False-positive and false-negative rates | Report the two error types separately when one is more harmful than the other. An overall accuracy figure can hide that difference. |
| Generated text or other context-dependent output | Human review against a defined rubric | Review correctness and task requirements using stated criteria. A fluent answer is not, by itself, proof of correctness. |
| Several user groups or operating conditions | Disaggregated results for relevant segments | Compare outcomes where the sample supports a meaningful comparison; document small samples and groups not tested. |
For example, a team evaluating a system that retrieves relevant records might care more about missing a relevant record than reviewing an extra result. A workflow that sends alerts may care about the burden of false alarms as well as missed events. These examples illustrate why the evaluation brief must identify consequences before choosing thresholds.
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3. Record the test conditions and limitations
For each run, record the system and model version, configuration, test-set description, expected answers, measures, results, and review method. Note relevant operating conditions and known limitations. If a vendor or product does not expose a stable version identifier, record the available date, configuration, and product details needed to identify the run as closely as possible, and mark that reproducibility is limited.
Report the size and composition of the sample alongside the result. A score from a narrow or small sample should not be presented as proof of performance for a broader population or a different setting. Re-run the evaluation when the model, interface, inputs, connected data, or intended use changes; deployed performance can shift as the system or its environment changes.
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Bias testing looks for harmful disparities in the system’s outcomes for groups relevant to the task. Begin by identifying affected groups and conditions based on the use case. Then compare the same kinds of errors or outcomes across those groups where the test data are sufficient. Do not treat a single average as evidence that a system is fair: it can conceal a substantially worse result for a subgroup.
Examine more than the model
A disparity may arise at several points, not only from the model’s calculations. Review the full chain:
- Data collection: Ask who or what is represented, who is missing, and whether the examples reflect the actual setting.
- Labeling: Check how expected answers were assigned, whether the criteria were applied consistently, and whether disagreement or ambiguity was recorded.
- Model and product choices: Consider how model selection, configuration, input handling, and output presentation may affect results.
- Human use: Examine whether reviewers interpret or act on outputs differently across people or circumstances, and whether the workflow gives them a realistic opportunity to challenge an answer.
The IC AI Ethics Framework asks whether data are representative and relevant to operational conditions and whether the system is evaluated for biased outcomes. Its guidance is useful for turning “check for bias” into questions about the test set and deployment context, rather than a claim based on one score.
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Make comparisons carefully
For each relevant group, report the number and type of examples, the outcome or error measures used, and the conditions tested. Explain which groups were not tested, why, and what the evaluation cannot establish. If a group’s sample is too limited for a reliable comparison, say so rather than inferring that performance is equivalent. Where privacy, data availability, or other constraints limit measurement, document the resulting blind spot and consider what other review is possible.
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Comparisons may expose trade-offs: changing a system to improve one measure or reduce a disparity can affect performance elsewhere. NIST notes that trustworthiness characteristics can trade off and that their relative importance varies by context. Record the trade-off, the affected groups, the decision-maker, and the reason a particular balance is acceptable for the intended use. Avoid declaring a tool “unbiased” on the basis of a limited evaluation.
Review privacy across the data lifecycle
Privacy is not answered by a product’s general privacy label or by the fact that a test account contains no obvious personal details. Map what happens to information from entry through use, storage, access, reuse, and deletion. UNESCO’s Recommendation on the Ethics of Artificial Intelligence calls for privacy and data protection throughout the AI lifecycle; NIST identifies confidentiality, anonymity, and user control as relevant privacy values.
Trace what enters and where it can go
Inventory the information users submit, including text, files, prompts, and data pulled through integrations or connected sources. For each category, find out whether the product retains it, who can access it, whether it can be reused, how deletion works, and what controls are available to the user or organization. Seek clear, product-specific documentation and verify settings in the product rather than assuming a broad policy description answers every operational question.
Until the relevant terms and safeguards are understood, use minimized or non-sensitive test data. Do not place real personal, confidential, or otherwise sensitive records into a trial simply because the task is only an evaluation. If sensitive information is necessary for a valid assessment, the organization should resolve its data-handling requirements and safeguards before running that test.
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Check controls in the actual configuration
Record the settings and integrations used during the evaluation, along with the information they govern. Confirm what a user can control, whether a connected source changes what information is available to the tool, and what the documented deletion process covers. If the product’s explanations leave a material question unanswered, treat that uncertainty as a decision factor rather than silently assuming the most favorable interpretation.
Privacy protections can affect other performance characteristics. NIST notes that privacy mitigations can trade off against accuracy under some conditions. Measure the system in the configuration you actually intend to deploy, and document both the privacy choices and any resulting performance change. A test with one configuration does not establish the performance or data handling of another.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test accessibility in the complete user journey
An accurate model can still be unusable if someone cannot reach the interface, understand its output, correct an error, or get help. Evaluate the complete user experience: entry points, prompts or forms, responses, status messages, error recovery, and any next action. For web interfaces, W3C’s Web Content Accessibility Guidelines (WCAG) are an established reference. Use current W3C materials rather than assuming a particular interface or AI interaction is accessible from a model score.
Check access, understanding, operation, and recovery
- Reach: Can users access the relevant controls and content, including when using assistive technology?
- Understand: Are instructions, status, limitations, and outputs presented in a way users can make sense of?
- Operate: Can people enter information, navigate results, and take the intended next step?
- Recover: Can users notice and correct mistakes, request a different format, or get out of a failed interaction?
- Escalate: Can a person reach human assistance when the tool is unclear, unsuitable, or wrong?
Test the actual interface and outputs, not only the provider’s accessibility statement. Where possible, involve disabled users and test with assistive technologies relevant to the intended audience. Record the task attempted, the barrier encountered, the user impact, and whether a workable alternative or recovery path exists. Testing with people who have access needs can reveal practical problems that a checklist alone may not expose.
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Check whether users can understand the system’s limitations, correct its output, obtain alternative formats, and reach a human. These are important parts of the user journey, but WCAG should not be presented as a complete AI-specific evaluation protocol. The guidance cited here establishes WCAG as a reference for web accessibility; it does not settle every question about accessibility in AI interactions. State that scope limit in the evaluation report and investigate additional needs raised by the particular product and users.
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Compare options and make a documented decision
Choose among tools, configurations, and a non-AI workflow using criteria tied to the intended task. Do not let a higher score on one dimension automatically override serious concerns elsewhere. NIST cautions that relevant characteristics can trade off and that their importance depends on context.
| Decision axis | Question to answer |
|---|---|
| Task performance | Does the tool meet the required task-specific measures, and how severe are false positives and false negatives? |
| Group outcomes | How do results compare for affected groups and conditions, and is the sample adequate for the conclusions? |
| Data handling | What is collected, retained, reused, accessed, and deleted, and what control do users have? |
| Accessibility | Can intended users complete the end-to-end task and recover from errors? |
| Transparency and repeatability | Can the tested version, configuration, data, and method be identified well enough to reproduce or audit the evaluation? |
| Human accountability | Who reviews, corrects, or escalates consequential outputs, and who owns the final decision? |
| Operational burden | What resources and process changes are needed, and do the expected benefits justify them in light of potential harms? |
Include “do not use AI for this task” as a real alternative. A tool may not justify its operational burden or risk if an existing process meets the need more safely, accessibly, or reliably. For a high-impact decision, do not make the model the unreviewable final authority: assign accountable human review and provide a way to contest or correct outputs.
Keep an evaluation record and set retest triggers
A useful report allows another person to understand what was tested, what the results mean, and what remains unknown. Keep a concise record that covers:
- Purpose, affected users, operating conditions, and consequences of error.
- Tool and model version, configuration, integrations, and test date.
- Test-set source and composition, expected outcomes, measures, and review method.
- Accuracy results and comparisons across relevant groups, including sample limits.
- Privacy data flows, controls checked, and unanswered handling questions.
- Accessibility tasks, methods, barriers, and recovery or human-support options.
- Known limitations, trade-offs, decision rationale, owners, and safeguards.
Define what changes require another evaluation. At minimum, reconsider the test when the model, interface, data, configuration, connected sources, or use changes. Also retest if the operating environment shifts or observed performance no longer matches expectations. NIST identifies ongoing testing and monitoring as ways to check whether deployed systems continue to perform as intended; the IC framework emphasizes version and data provenance for repeatability and audit.
“Every system must be tested for accuracy in an environment that controls for known and reasonably foreseeable risks prior to being deployed.”
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