In-Processing Algorithmic Fairness & Optimization Constraints
Unofficial practice on fairness interventions applied during model training. This set examines constrained optimization, adversarial objectives, joint representation learning and how to assess the resulting trade-offs. Topics include fairness-penalty weights, empirical constraints, risk-score compatibility limits and nondominated candidate models. Scenarios connect technical choices to approval responsibilities, validation evidence and the limits of treating a metric as a complete account of fairness. Legal documentation questions distinguish applicable requirements from particular optional analytic techniques. Basic familiarity with supervised learning, error rates and group-based fairness metrics is useful. Practise recognizing when a training result fails to generalize, identifying intersectional gaps and responding to infeasible requirements. The material supports IAPP AIGP version 2.1 competencies on training/testing risks, metric evaluation and documented governance decisions. It is independent study material; no selected optimization method alone establishes non-discrimination or legal compliance.
Oct 22, 2026
