Fairness, Non-Discrimination & Algorithmic Equity
Unofficial AIGP practice on algorithmic fairness and non-discrimination. Compare group and individual fairness, identify proxy and aggregation bias, classify mitigation stages, and evaluate trade-offs in credit, healthcare and justice scenarios. The questions distinguish demographic parity, equal opportunity, equalized odds and predictive parity. They also examine the limits of simultaneous fairness criteria, the EEOC four-fifths screening rule, and the narrowly conditioned special-data provision in EU AI Act Article 10(5). Metric satisfaction should be considered alongside context, harms and applicable law. Basic familiarity with classification errors, protected groups and personal-data concepts is useful. The retained material supports practice in the responsible-AI principles covered by the IAPP AIGP Body of Knowledge version 2.1; it is not an official question bank or a comprehensive exam simulation. Additional scenarios examine intersecting groups, small-sample uncertainty, protected-attribute proxies, changing populations and accessibility concerns raised by affected people.
Oct 22, 2026
