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. State the three group-fairness criteria (demographic parity, equalized odds, conditional use accuracy equality) and one impossibility result. Describe individual fairness via the Dwork et al. 2012 Lipschitz formulation. Describe counterfactual fairness and its causal-graph dependency. Explain backtracking counterfactuals and why they sidestep the intervention-on-protected-attribute problem. Lesson 20 was about measuring bias. Lesson 21 is about defining the fairness standard the measurement should serve. The three families give structurally different standards — a model can be group-fair and individual-unfair, counterfactually fair and group-unfair. Choosing a standard is a policy decision; no standard is universally optimal. Demographic parity. P(Y=1 | A=a)…
Fairness Criteria — Group, Individual, Counterfactual: Three families structure the fairness literature. Group fairness: demographic parity, equalized odds,…
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