Track provenance, licenses, and consent for every corpus. Remove sensitive identifiers, minimize collection, and honor do‑not‑train signals. Run a Data Protection Impact Assessment, write a clear purpose statement, and limit reuse. When challenged, show your paper trail confidently instead of improvising a late, brittle justification.
Define representative cohorts and outcome thresholds. Use counterfactual prompts, subgroup error analysis, and red‑teaming to expose skew. Balance precision with coverage, calibrate confidence, and track harms beyond accuracy. Publish findings internally, then fix upstream issues, retrain, and retest until disparities narrow in meaningful, durable ways.
Place skilled editors as reviewers and decision owners. Provide escalation paths, time for thoughtful judgment, and clear override controls. Offer users explanations and easy opt‑outs. Human accountability closes gaps algorithms miss, preventing quiet failures from amplifying into public crises or inaccessible, irreversible outcomes.