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Evaluate a decision-scoring model against the specific decision it will inform, using representative data and evidence about errors, consistency, and outcomes for affected groups. A single accuracy or fairness score cannot establish that a model is suitable: the consequences of mistakes, the people evaluated, and the conditions of use all matter. This guide sets out a practical evaluation sequence and explains what to document before and after deployment.
1. Define the decision the score is meant to support
A model is not accurate or fair in the abstract. Its performance must be assessed for a particular intended use: what decision the score informs, how a person or organization uses it, and who may be affected.
Before testing, record:
- The decision or ranking the score supports and the outcome it is intended to predict or estimate.
- Who will act on the score, who will be assessed, and whether a human reviews the result or the score determines the outcome directly.
- How the score will be used, including any cutoff, ranking rule, or other operational threshold.
- The consequences of false positives and false negatives in this setting. For example, a false positive means the model indicates a positive outcome when the observed outcome is negative; a false negative means it misses a positive outcome.
- The population, location, and operating conditions covered by the intended use.
These choices determine which errors matter and what evidence would be relevant. A model suitable for prioritizing cases for human review may not be suitable for making the final decision automatically.
2. Design a test that reflects real use
Keep evaluation data separate from the data used to develop or tune the model. The test data should represent the people, time periods, inputs, and operating conditions expected after deployment. A test set that differs substantially from real use can make performance look better or worse than it will be in practice.
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Document the data and test design so another evaluator can understand what the results cover:
- When the data were collected and which populations or settings they include.
- How cases were included or excluded, how missing data were handled, and how the target outcome was defined.
- Known limits in coverage, measurement, or labels, and any conditions not represented in the test.
- The model version, configuration, and scoring conditions used to produce the results.
Use NIST’s AI Risk Management Framework (AI RMF) as voluntary guidance, not as a certification or guarantee of performance. NIST describes validity, reliability, and fairness—with harmful bias managed—as trustworthiness concerns across the AI lifecycle. Its AI RMF 1.0 was released in 2023, and NIST’s framework page says it is being revised.
3. Measure accuracy in terms of the decision
Accuracy means closeness to an accepted true value. NIST AI RMF 1.0 quotes ISO/IEC TS 5723:2022 for that definition. For a decision-scoring model, the practical question is which errors affect the intended decision and how often they occur on realistic, representative test data.
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For decisions with a positive or negative outcome
Report a confusion matrix or the relevant error rates, including false positives and false negatives. The table shows the four possible comparisons between a model’s prediction and the observed outcome; it does not imply that the errors have equal cost.
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| Observed positive outcome | Observed negative outcome | |
|---|---|---|
| Model predicts positive | True positive | False positive |
| Model predicts negative | False negative | True negative |
Choose and interpret rates in light of the decision. For instance, false positives may be especially consequential when a positive score leads to a restrictive action; false negatives may matter more when the purpose is to identify people who need support. State the denominator and the test population for every reported rate so readers can see what it measures.
For ranked scores or probabilities
If the score ranks cases, examine whether higher scores correspond to higher observed rates of the outcome. If the score is presented as a probability, assess calibration: among cases assigned similar probabilities, do observed outcomes occur at roughly corresponding rates? These checks answer different questions from whether a model gets a final classification right at one cutoff.
Disclose the test method, the data conditions, and any segments used to break down results. NIST recommends realistic, representative test sets and notes that accuracy measures may be disaggregated by segments. No single reported rate should stand in for the full error profile.
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Consistency means more than obtaining the same score twice. Test whether the model performs as required under the conditions it is expected to face and over time. NIST describes reliability in relation to performance under given conditions over time, and robustness as maintaining performance across circumstances.
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- Repeat scoring: If the system is expected to be deterministic, score the same inputs again under the same version and configuration. Investigate unexplained differences.
- Reasonable input changes: Test small, plausible changes in inputs to see whether they cause unexpectedly large score changes or reverse the decision.
- Relevant groups and settings: Check performance for the populations and operating conditions that matter in the intended use, not just for the overall test set.
- Time periods: Compare results across appropriate periods to detect changes in input patterns or observed outcomes.
- Operational conditions: Evaluate the system in the settings, workflows, and configurations in which it is expected to run.
Log the model version, input data, configuration, and conditions for each evaluation. Without that record, it can be difficult to distinguish a change in the model from a change in data or operating practice.
5. Examine group differences and possible bias
Compare score distributions, selection or decision rates, and relevant error rates across groups that matter in the use context. A favorable overall result can conceal a different error pattern or outcome for a particular group. Neither aggregate accuracy nor passing one fairness statistic establishes that a model is unbiased or appropriate.
When differences appear, investigate plausible sources rather than treating the score as the explanation. NIST’s 2022 publication, Towards a Standard for Identifying and Managing Bias in Artificial Intelligence (SP 1270), discusses dataset, measurement, and human-factor challenges. Consider whether observed differences may relate to:
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- How inputs and outcomes were measured, including missingness or label quality.
- Choices in system design and the way scores are converted into decisions.
- How people interpret, rely on, or override the score in practice.
Choose group comparisons based on the decision, affected population, and applicable context. Include disability where relevant; NIST’s Measure playbook highlights disability inclusion as well as differences in group outcomes. Fairness measures can address different concerns, so select and interpret them in context rather than treating any one measure as a universal verdict.
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6. Inspect consequential errors and real-world use
Aggregate metrics show patterns, but they do not explain individual cases. Review consequential errors and edge cases to understand how they arose, while protecting personal information and following applicable rules. Consider whether operators can understand what the score means and what it does not mean, whether they know its limits, and whether the interface or workflow encourages overreliance.
Record human overrides and how they affect decisions. A technically sound score can still be used poorly if staff mistake it for certainty, ignore relevant information, or apply it outside the conditions for which it was evaluated. NIST treats bias and validity as socio-technical concerns; it does not prescribe one audit protocol for every use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Set acceptance rules, document limits, and monitor
Define acceptable performance and fairness criteria before reviewing final test results. Those criteria should reflect the decision’s consequences and be agreed by the people accountable for the decision, with input from affected stakeholders. There is no universal accuracy rate, error-rate gap, or fairness threshold that makes every scoring model acceptable.
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Document the evidence, trade-offs, limitations, and conditions of use. After deployment, monitor whether inputs, outcomes, error patterns, or operating conditions change. Reevaluate after material changes, including retraining or changes to the workflow. NIST’s AI RMF Measure playbook offers implementation suggestions, not binding requirements, and NIST advises considering evaluation across the lifecycle.
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How to compare two scoring models
Compare alternatives on the same representative held-out data and for the same intended use. Use a shared decision context; otherwise a difference in results may reflect different test conditions rather than a meaningful model advantage.
| Comparison area | What to examine |
|---|---|
| Decision errors | False-positive and false-negative rates, interpreted against the consequences of each type of mistake. |
| Score quality | Calibration for probability scores or ranking quality for ranked scores, when relevant to the use. |
| Group outcomes | Differences in relevant outcomes and error rates across affected groups. |
| Stability | Performance over time and across expected operating conditions. |
| Use in practice | Whether people can understand the score’s meaning and limits and use it appropriately. |
| Ongoing oversight | Monitoring burden and documented limitations. |
A model with a better aggregate score may still produce a less acceptable error profile for a particular group or a more harmful pattern of mistakes. NIST also notes that accuracy and robustness can be in tension, so compare the trade-offs relevant to the intended use rather than selecting by one headline number.
Keep legal conclusions separate from performance results
Technical evaluation can inform a legal review, but it does not by itself establish compliance. The applicable rules depend on the decision, jurisdiction, and sector.
For employment selection in the United States, EEOC guidance describes validation as showing that a selection procedure measures skills, knowledge, or abilities related to job performance. It also says employers should consider suitable alternative procedures or uses with substantially equal validity and less adverse impact. This is employment-specific US guidance; it is not a general legal test for credit, healthcare, housing, education, public benefits, or other jurisdictions.
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