Partly—and not with one better leaderboard. AI evaluations can become more trustworthy when their designers define exactly what they intend to measure, test whether the test measures it, disclose uncertainty and conditions, use protected data where appropriate, and compare predictions with what systems do after deployment. These steps can reduce known weaknesses, but no single benchmark or score can establish that an AI system is trustworthy in every setting.
What is the AI evaluation crisis?
AI benchmark scores increasingly influence decisions about products, investment, procurement and policy. The problem is not that benchmarks are useless. It is that a score can look precise while measuring something different from what its label implies—or perform poorly as a guide to behavior outside the test.
Stanford researchers reported that evaluations claiming to measure the same capability can disagree; their study examined 56 widely used benchmarks. The report describes the findings ahead of the researchers’ scheduled October 2026 conference presentation, so its account supports the broad concern but not claims about details beyond what the interview reports. Stanford Report, September 25, 2026.
One example is BBQ, a multiple-choice benchmark used to assess bias. Some questions intentionally leave out information and expect the answer “we don’t know.” A model may make an assumption based on gender and be marked biased; another may recognize the question is underspecified and score as unbiased. As Stanford computer scientist Sanmi Koyejo put it, “What it ends up measuring is closer to reading comprehension than to bias, and that’s a benchmark not measuring the thing its name promises.” This is a construct-validity problem: the score may be influenced by a capability other than the one the benchmark is meant to measure. It does not, by itself, show that BBQ has no use.
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Why a high benchmark score may not predict real-world reliability
A benchmark measures performance on a particular set of tasks, under particular conditions. Changing the prompts, task format, data, users or deployment environment can change what the result means. A model that performs well on a test may still fail when a real user asks a differently worded question, supplies incomplete information or relies on the answer in a consequential workflow.
NIST identifies several open measurement problems: whether an evaluation captures its intended construct; whether results generalize beyond the test setting; how to communicate uncertainty; which baselines make comparisons meaningful; how to compare evaluations; and whether pre-deployment results relate to post-deployment outcomes. A score is evidence about tested performance, not a guarantee of field performance. NIST CAISI, December 2, 2025.
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Several distinct qualities also get compressed too readily into a single idea of “trustworthiness.” NIST lists accuracy, interpretability, privacy, reliability, robustness, safety, security and mitigation of harmful bias as separate characteristics whose measurement depends on context. A system can perform well on one and poorly on another; an evaluation should say which quality it addresses. NIST AI Measurement and Evaluation.
What would make an AI evaluation more credible?
Think of a benchmark as a measurement instrument, not a verdict. Before relying on a result, ask whether its design supports the decision you want to make.
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- Define the decision and the capability or risk. State what the evaluation is meant to inform, which system behavior matters, and which users and conditions are in scope. “Measures bias” is too broad unless the evaluation specifies the kind of bias, the situations tested and how answers are judged.
- Check construct validity. Look for ways the test can reward or penalize an unrelated skill, such as reading comprehension, prompt-following or familiarity with a test format. Ask whether the items and scoring rule actually distinguish the capability named in the benchmark.
- Test sensitivity and generalization. Examine whether modest changes in prompts, tasks or conditions alter the result, and whether the test resembles the intended use. A score from one setup should not silently stand in for performance across different settings.
- Consider contamination and repeated exposure. If test questions or answers may have appeared in training data, strong performance may not show that a model can handle unseen tasks. Sequestered or blind test data can reduce this risk, though it does not resolve every validity problem.
- Report uncertainty and methodology. Explain how the evaluation was run, how results were scored, what uncertainty remains and what limits comparison. A result without enough methodological detail is difficult to judge or reproduce.
- Choose relevant baselines. Compare against appropriate human or non-AI alternatives where relevant, rather than treating a model ranking as meaningful on its own. The useful baseline depends on the task and decision.
- Check predictions after deployment. Where possible, compare evaluation results with observed outcomes in the setting the system is intended to serve. This helps reveal failures a pre-deployment test did not capture.
These are measurement priorities and research needs identified in NIST’s discussion, not a set of universally settled rules. Their importance varies with the system, risk and use case.
What NIST guidance can—and cannot—do
NIST’s AI 800-2 announcement describes an initial public draft of voluntary practices for automated benchmark evaluations. The draft organizes the work into defining objectives and selecting benchmarks, implementing and running evaluations, then analyzing and reporting results. It is aimed mainly at technical staff evaluating AI systems, including developers, deployers and third-party evaluators. NIST notes that automated benchmarks can be useful when time, expertise or resources are constrained, but cannot meet every evaluation objective. The announcement was updated February 10, 2026, and said the draft comment period would close March 31, 2026; it should not be mistaken for a final standard. NIST AI 800-2 announcement.
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That scope matters. A common automated test can make one kind of comparison more consistent, but it cannot answer every question about safety, privacy, security, bias or performance in a specific deployment. Organizations still need to match evaluation methods to their objectives and report what remains untested.
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Sequestered testing can reduce one contamination risk
NIST’s Artificial Intelligence Technology Evaluation (AITE) program offers volunteer model testing in a sequestered testbed using blind data, shared data, metrics and scoring. Its page lists 2026 program tests including quantum-dot patches (641 trials), genome-variant visualization (10,000 trials) and public-safety visual-event recognition (3,000 trials). These figures describe the listed trial counts, not error rates or proof that the approach succeeds; the tasks are program-specific rather than a universal benchmark. NIST AITE.
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Agentic AI probes can audit claims against evidence
NIST describes ongoing work on evaluation probes for agentic AI that compare an agent’s factual claims with a human-curated reference corpus and create an evidence audit trail. Its demonstration rubric asks whether the source supports a claim (faithfulness), whether the account captures the source’s message (completeness) and whether the evidence carries the claim’s burden (sufficiency). This is an emerging project, not a validated, off-the-shelf fix for agent evaluation. NIST project description, updated May 5, 2026.
How to choose an evaluation for a real decision
When comparing two or more evaluation options, compare their properties against the intended use—not just their scores or popularity.
| Question | Why it matters |
|---|---|
| Does it measure the intended construct? | A test can produce a clear score that is driven partly by an unrelated skill. |
| Are results reliable and reproducible? | Results that shift with small changes or cannot be reproduced are weak grounds for comparison. |
| Is it resistant to contamination? | Exposure to test items can make performance on familiar data a poor indicator of performance on unseen tasks. |
| Does it fit the domain and deployment setting? | Performance on a generic test may not represent behavior with the intended users, tasks or constraints. |
| Does it report uncertainty and use relevant baselines? | Without uncertainty and a meaningful reference point, a score or ranking can be hard to interpret. |
| Can results be checked against field outcomes? | Post-deployment comparisons show whether a pre-deployment evaluation predicted behavior that matters in practice. |
| Can the evaluation be run within practical limits? | Time, expertise and resources constrain what can be evaluated; automated benchmarks can help, but their limits should be explicit. |
No one evaluation option wins on every dimension. A responsible choice explains the decision it supports, the conditions it covers and the important questions it leaves unanswered.
So, can we fix it?
We can improve AI evaluation by applying measurement discipline: validate what a test measures, make its conditions and uncertainty visible, protect data when contamination is a concern, select baselines suited to the decision and check results against deployment outcomes. The harder task is resisting the temptation to treat any score as a complete account of a system. As Koyejo argued, “Over the years, measurement science has gotten very good at making sure every test item precisely measures specific capabilities. We want the AI field to bring the same rigor to benchmarking.”
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