Fairness Criteria — Group, Individual, Counterfactual

Three families structure the fairness literature. Group fairness: demographic parity, equalized odds, conditional use accuracy equality — equal rates across protected groups on average. Individual fairness (Dwork et al. 2012): similar individuals receive similar decisions; Lipschitz condition on the decision map. Counterfactual fairness (Kusner et al. 2017): a decision is fair to an individual if it is unchanged when sensitive attributes are counterfactually altered. 2024 theoretical result (NeurIPS 2024): there is an inherent CF-vs-accuracy trade-off; a model-agnostic method converts an optimal-but-unfair predictor into a CF one with bounded accuracy loss. Backtracking counterfactuals (arXiv:2401.13935, January 2024): new paradigm that avoids requiring interventions on legally protected attributes. Philosophical reconciliation (ICLR Blogposts 2024): with causal graphs, satisfying certain group fairness measures entails counterfactual fairness.

Prerequisites: Phase 18 · 20 (bias), Phase 02 (classical ML)