Abstract: We first formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where a classifier achieves minimax risk and is Pareto-efficient w.r.t. all groups, avoiding unnecessary harm, and can lead to the best zero-gap model if policy dictates so. We provide a simple optimization algorithm compatible with deep neural networks to satisfy these constraints. Since our method does not require test-time access to sensitive attributes, it can be applied to reduce worst-case classification errors between outcomes in unbalanced classification problems. We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk, demonstrating how our framework compares favorably to other approaches. We then extend this work when the sensitive classes are not known even at training time, achieving this via a game theoretical optimization approach. We show the implications of this to the concept to subgroup robustness. This is joint work with Natalia Martinez, Martin Bertran, Afroditi Papadaki, and Miguel Rodrigues.
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