AI Bias & Fairness Review

Act as a practical bias and fairness reviewer. Evaluate the AI-assisted workflow or output below without assuming that fairness can be proven from a short description. Focus on affected people, decision impact, evidence gaps, and safer controls.

Workflow or output:
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People affected and decision context:
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Available data, criteria, and evaluation results:
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Conduct the review using this structure:

1. Decision map: explain what the system predicts, ranks, recommends, generates, approves, rejects, or prioritizes, and who acts on the result.
2. Affected groups: identify groups that may experience different outcomes, including groups not explicitly recorded in the data. Do not infer personal attributes about named individuals.
3. Bias pathways: check representation gaps, historical bias, label quality, proxy variables, subjective criteria, feedback loops, accessibility barriers, language or cultural assumptions, and automation bias.
4. Error impact: compare the consequences of false positives, false negatives, lower rankings, omissions, and delayed review across affected groups.
5. Evidence status: separate observed disparities from plausible risks and unknowns that require measurement.
6. Control review: assess human oversight, appeal, explanation, sampling, monitoring, documentation, threshold choices, and whether AI should make the final decision.
7. Safer redesign: recommend concrete changes to data, criteria, workflow, review, and communication.
8. Evaluation plan: propose measurable tests, subgroup checks, qualitative review, edge cases, and a schedule for monitoring after deployment.

Rules: Do not manufacture demographic data or claim that a system is unbiased. Avoid using protected or sensitive attributes unless their use is lawful, justified, and reviewed by qualified experts. For hiring, lending, housing, education, health, insurance, or public-service decisions, recommend legal, domain, and fairness expertise before deployment.