| name | ai-fairness-bias-evaluator |
| description | Design and assess evidence-backed fairness and bias evaluations for AI, LLM, recommendation, ranking, or agent behavior. Use when a responsible-AI tester needs approved subgroup, paired counterfactual, intersectional, allocation, quality-of-service, or stereotype tests using synthetic or consented data. |
| license | MIT |
| metadata | {"author":"TheTestingAcademy","category":"AI Safety and Guardrails","version":"1.0.0"} |
AI Fairness and Bias Evaluator
Surface measurable disparities and qualitative harms without inventing demographic data,
claiming causality, or turning a sampled evaluation into legal or compliance certification.
Workflow
- Confirm the decision context, affected population, system owner, approved attributes,
applicable supplied policy, privacy/legal reviewers, authorized staging environment,
and prohibited data uses.
- Record the build, model/provider and version, prompt/configuration revision, guardrail,
dataset/corpus provenance and version, sampling method, locale, evaluator, and run ID.
- Define a harm hypothesis and a cited expected behavior for each approved attribute and
intersection. Mark missing requirements
Not specified; do not infer protected groups or
fairness thresholds from general knowledge.
- Prefer paired synthetic counterfactuals that vary only the approved attribute. Use consented,
de-identified data only when explicitly approved; document sample limits and known proxies.
- Capture per-case outputs and compute pre-approved subgroup rates, deltas, denominators,
uncertainty, missing groups, and reviewer disagreements. Preserve qualitative examples in
redacted form.
- Separate observed disparity from inferred pattern and causal hypothesis. Recommend further
review or mitigation, then require a fresh evaluation after material changes.
Evidence register
Classify every statement as Observed, Inferred, Hypothesis, or Not tested.
| Classification | Source / run ID | Expected control | Observed result | Evidence artifact | Unresolved gap |
|---|
| Observed / Inferred / Hypothesis / Not tested | pair/subgroup and run ID | cited fairness requirement | output, rate, delta, uncertainty, or no run | redacted cases and calculation | sample, proxy, intersection, or reviewer gap |
Record exact build, model, prompt, policy, guardrail, dataset, sampling, metric, evaluator,
locale, and environment versions. Always show counts and denominators with aggregate rates.
--- HUMAN REVIEW GATE ---
Unverified items:
Remaining risks:
Required approver:
Decision: approve / revise / reject
Guardrails
- Run only in an explicitly authorized staging or sandbox environment using synthetic inputs by
default. Never infer or generate sensitive attributes for real people.
- Never fabricate a policy, demographic, label, test result, metric, threshold, source,
reviewer decision, or evidence link. Mark missing facts and groups explicitly.
- Never claim safe, fair, unbiased, compliant, causal, representative, or fully tested from
sampled results. Avoid ranking groups or presenting disparity as a trait of people.
- Require domain, privacy, legal, and system owners to review attribute choices, interpretation,
mitigation, and release impact.
