What can an AI safety evaluation tell you?

Start with a concrete question

An evaluation is useful when it tests a defined behavior in a defined setting. “Does this system expose a private field when summarizing these documents?” is more actionable than “Is this model safe?”

Look at both kinds of error

A safety classifier can miss risky material or wrongly reject benign material. Which error is more costly depends on the application. Evaluate both, and inspect examples rather than only an aggregate number.

The setup matters

Policy wording, model version, language, input length, tool permissions, and test distribution can change results. Record the conditions so readers know what the result does and does not establish.

Ask what is in the denominator

A result of 95% means little without knowing 95% of what. Was the test made of attacks, ordinary requests, selected failures or a mixture? Were refusals counted as successes? Were languages and task types represented? Our recommendation is to look for the full setup before comparing headline scores across different reports.

Both kinds of error have costs

Consider a hypothetical 1,000 requests, of which 10 are genuinely harmful. A checker catches eight, misses two, and incorrectly flags 50 harmless requests. It catches 80% of the harmful requests, but only eight of its 58 flags are harmful. These invented numbers show why detection rate alone cannot describe review workload or user friction.

Preserve the conditions of the test

Record the model version, policy, input selection, tools, scoring rules and date. Then re-test important cases after changes. NIST’s profile situates measurement within ongoing risk management; it does not turn a single benchmark run into a lasting certificate of safety.

Questions to take with you

• Find the test population, exact task and scoring rule.
• Look at false positives, missed risks and the most consequential failures.
• Check whether the reported setup resembles the product you plan to use.

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