Bias evaluation asks whether an AI system's behavior differs across groups in ways that matter for your use case — demographic groups where the stakes are individual people, or use-case subpopulations (customer segments, document types, dialects, regions) where the stakes are quality and fairness of service. Model providers evaluate their models in general; Anthropic, for instance, has had its AI management system certified under ISO/IEC 42001, the international standard for AI governance. But no vendor evaluation covers your system: your system prompt, your retrieval data, and your user population all shape behavior. What follows is a practical review process, offered as recommended practice — not as a method that satisfies any specific legal or regulatory fairness requirement.
Step 1: decide what disparity would mean here
Bias is use-case-specific, so start by writing down, for each AI use case, the answer to three questions: Which groups or subpopulations could plausibly be treated differently? What output dimensions matter (accuracy, tone, refusal rate, length, recommendation content)? And what decision or experience does the output feed — because a tone difference in a marketing draft and a substantive difference in anything touching hiring, lending, housing, or healthcare are entirely different severities. High-stakes domains warrant legal review of the evaluation plan itself, and are exactly where human decision-makers, not model outputs, must own the outcome.
Step 2: build paired test sets
The core technique is simple: construct sets of test inputs that are identical except for the attribute under study — the same customer complaint with different names, the same résumé summary with group signals varied, the same question in different dialect registers — and run them through your actual production configuration, including your system prompt and any retrieval context. Realistic inputs drawn from (properly anonymized) production traffic beat synthetic ones; supplement them with deliberately difficult cases. Aim for enough examples per group that a difference is a pattern rather than an anecdote, and be honest in your documentation about how large your samples were.
Reproducibility matters for defensibility, and here the platform helps: every Claude model ID is a pinned snapshot, so recording the exact model ID alongside your prompts, parameters, and test set means the configuration you evaluated is the configuration you can name later. Record the platform too (first-party API, Bedrock, Vertex AI, Foundry), since your deployment surface is part of the system under test.
Step 3: measure, with humans in the loop
Score outputs on the dimensions you defined: task accuracy per group, refusal or hedging rates, sentiment and tone, and substantive content of recommendations. Automated scoring (including using a model to grade outputs against a rubric) scales the process, but every automated finding of disparity — and a sample of "no disparity" results — should be reviewed by humans, because graders can themselves be systematically wrong. Where possible, have reviewers grade blinded to which group an output belongs to.
Step 4: document what you found — including nothing
The evaluation record should let a future reader reconstruct what was tested and judge the conclusion: model ID and platform, prompt version, test set description and size, metrics, results per group, reviewer notes, and the disposition. Document null results too; "we tested for X and found no meaningful difference, on this sample, on this date" is valuable evidence of a functioning process, and the record set is precisely what internal audit or an examiner will want to see (see evaluation documentation).
Step 5: address, then re-test on every change
When you find a disparity, work the options in order of directness: adjust the system prompt or instructions; constrain the output format so the disparity-prone dimension is controlled; add retrieval or context that grounds the model in your actual policy; add a human checkpoint for affected paths; or restrict the use case's scope. Whatever you change, re-run the evaluation to confirm the fix — and re-run it whenever the model version, prompt, or upstream data changes materially. A bias evaluation is a snapshot; migrations are exactly when regressions arrive (make it part of your model change management).
Where to go next
This process slots into your broader testing discipline — see evaluation and testing and model validation — and its outputs belong in your AI registry entry for each use case.