Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsTest an AI system against the job it will actually do: use realistic, independent test data; measure the errors that matter; check results across relevant groups and operating conditions; and continue testing after launch. An overall score alone cannot show whether a system is dependable or safe for every person and situation it may affect.
What accuracy, reliability, robustness, and bias mean in a test
These terms describe different questions. A system can score well on one and still fail another, so define what each means for the particular use before choosing tests.
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- Accuracy: How often does the system produce the right result for a defined task? “Right” must be judged against a suitable reference, such as an adjudicated label or a task-specific human assessment.
- Reliability: Does it perform as required over a specified period and under specified conditions, without failures that prevent the task from being completed? NIST’s AI Risks and Trustworthiness guidance cites this as the standard meaning of reliability.
- Robustness: Does performance hold up across expected variations and plausible disruptions, such as incomplete inputs or changes in operating context?
- Bias and disparate impact: Do data, design, or deployment choices produce unfair outcomes or harms for particular people or groups? NIST’s Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (SP 1270) frames bias as both technical and socio-technical, with potential harms in areas such as hiring, health care, and criminal justice.
For each measure, state what was tested, on which data, under which conditions, and how outcomes were judged. NIST’s AI Resource Center advises pairing accuracy measurements with clearly defined, realistic test sets representative of expected use and documenting the methodology.
1. Define the system and its intended use
Test the whole decision path
Write down the boundary of the system being evaluated. Depending on the product, that may include the model, preprocessing, prompts or rules, user interface, external tools, human review, and the downstream decision process. A model-only score will not capture errors introduced by an interface, an integration, or a human workflow.
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Describe the intended purpose, users, people affected by outputs, operating environments, and what happens after an output is produced. Be specific about where the system is not intended to be used. These details determine which examples, conditions, and risks belong in the evaluation.
Decide which errors matter
Identify the possible harms of incorrect, delayed, missing, or misleading outputs. For a classifier, decide whether false positives, false negatives, or both are consequential. For a generative system, define task success and the output types that are unacceptable; use human evaluation when automated scores cannot represent the real task outcome. If people will review or act on outputs, evaluate the human-AI workflow as well as the model in isolation.
2. Build a representative test set
Set aside examples that were not used to train or tune the system. The test data should resemble the population and inputs expected in deployment, including relevant languages, devices, workflows, and operating conditions. A test set that is easy to score but unlike real use can give a misleading impression of performance.
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Record the sample size and the groups, settings, and input types represented. If a relevant population or condition is absent, report that gap rather than treating performance as established for it.
3. Choose task-appropriate measures and report uncertainty
Pick measures that reflect the task and the consequences of mistakes. Report the denominator, test conditions, sample size, and uncertainty alongside any headline result. NIST’s AI RMF Measure guidance calls for measures of uncertainty, comparisons to benchmarks, and formal reporting.
For classification
Include the confusion matrix and class-level results, not only overall accuracy. Report false-positive and false-negative rates; precision and recall may also be useful, depending on the task. These measures reveal different failure patterns: for example, a system may miss many positive cases while still appearing accurate if positive cases are rare.
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For generative or human-facing systems
Define task success and unacceptable outcomes before scoring. Use a consistent rubric and qualified reviewers when quality, safety, or usefulness requires human judgment. Record how disagreements were resolved. For systems used alongside people, test how the system changes decisions and outcomes in the actual workflow rather than assuming model scores predict those effects.
When comparing results, distinguish a meaningful change from one that could plausibly be due to sample variation. Do not treat a small observed difference as conclusive without considering sample size and uncertainty.
4. Test reliability and robustness over conditions and time
Repeat evaluations across realistic conditions and, for deployed systems, at intervals over the system’s operating life. Set an operating envelope: the input and environment conditions under which the system is expected to work. Include plausible cases near its limits, such as poor input quality, missing information, unusual but valid examples, workload changes, and upstream data or integration problems.
Track performance by condition and note where it degrades or fails. Reliability concerns whether the system performs as required over time under specified conditions; robustness concerns whether it maintains useful performance as circumstances vary. A one-time test cannot establish either property for all future conditions.
For higher-impact uses, rehearse what happens when the system is wrong or cannot complete the task. Depending on the application, safeguards may include detecting the failure, escalating to a person, allowing human intervention, rolling back a change, or shutting the system down safely. NIST’s trustworthiness guidance highlights the need for human intervention when a system cannot detect or correct errors.
5. Look for disparities and harmful bias
Disaggregate outcomes for groups and contexts relevant to the system’s use and the people it may affect. Compare error rates and, where possible, examine the consequences of errors—not just their frequency. Which groups to examine depends on the application and available evidence; no single subgroup list or fairness measure is appropriate for every system.
Check whether the test data and labels represent the task fairly. Consider how collection, historical patterns, design choices, and deployment context may interact to produce harm. Involve relevant domain experts and, where appropriate, affected communities when identifying likely impacts and interpreting results. NIST’s AI RMF treats fairness and bias evaluation as context-dependent rather than prescribing one universally decisive metric.
Report which groups and situations were evaluated, what disparities were observed, and where evidence is insufficient. An aggregate score can conceal unequal error rates or unequal consequences.
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Benchmarks can miss failures triggered by adversarial prompts, misuse, environmental context, user interaction, or workflow integration. Add tests that challenge the system with plausible misuse and difficult cases, and observe it in the setting where it is intended to operate.
NIST’s AI Risk and Incident Assessment (ARIA) program describes three evaluation levels: model testing, red-teaming, and field testing. These are useful categories for going beyond raw performance scores, not a guarantee that any particular test suite covers every risk. Record the scenarios tested and the limitations of the exercise.
7. Document results and keep testing after launch
Maintain a record that lets another reviewer understand what was tested and why. Include:
- System version and the task and use case being evaluated.
- Data provenance, selection criteria, split, labels, and known gaps.
- Metrics, test methods, tools, conditions, sample sizes, and uncertainty.
- Overall and subgroup findings, failure cases, and known limitations.
- Acceptance rationale, unresolved risks, and any safeguards or human review required.
NIST’s AI RMF Core, Measure section calls for documenting test sets, metrics, and evaluation tools. It also recommends testing before deployment and regularly during operation, with production monitoring, feedback channels, and reassessment.
After launch, monitor changes in inputs, model behavior, operating context, user feedback, and incident patterns. Re-test after material changes to the model, data, policies, or workflow. Revisit the evaluation itself as methods, knowledge, risks, and impacts change; a test plan is not permanently valid just because it was thorough at launch.
How to decide whether the evidence is sufficient
Set acceptance criteria before reviewing results, based on the intended use, severity of potential harms, and applicable domain requirements. Consider task performance, population coverage, robustness, operational safeguards, evidence quality, and the consequences of residual errors together. NIST’s AI Risk Management Framework (AI RMF) 1.0, released January 26, 2023, is voluntary U.S. government guidance; it does not establish a universal pass score. Its AI Resource Center indicates the framework is being updated, so consult current NIST materials when applying it. The framework does not replace applicable law, regulation, sector standards, or domain-specific validation requirements, which depend on the application and geography.
When comparing two systems or evaluation plans, compare them on the same task definition and under the same conditions. A higher score is not meaningful if one test used different data, omitted relevant groups, or measured a less consequential error. Independent review can also strengthen testing and help mitigate internal bias or conflicts of interest, as the AI RMF Core notes.
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