SLIDE: a surrogate fairness constraint to ensure fairness consistency

• URL: http://arxiv.org/abs/2202.03165v1
• Date: Mon, 7 Feb 2022 13:50:21 GMT
• Title: SLIDE: a surrogate fairness constraint to ensure fairness consistency
• Authors: Kunwoong Kim, Ilsang Ohn, Sara Kim, and Yongdai Kim
• Abstract summary: We propose a new surrogate fairness constraint called SLIDE, which is feasible and achieves a fast convergence rate. Numerical experiments confirm that SLIDE works well for various benchmark datasets.
• Score: 1.3649494534428745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As they have a vital effect on social decision makings, AI algorithms should be not only accurate and but also fair. Among various algorithms for fairness AI, learning a prediction model by minimizing the empirical risk (e.g., cross-entropy) subject to a given fairness constraint has received much attention. To avoid computational difficulty, however, a given fairness constraint is replaced by a surrogate fairness constraint as the 0-1 loss is replaced by a convex surrogate loss for classification problems. In this paper, we investigate the validity of existing surrogate fairness constraints and propose a new surrogate fairness constraint called SLIDE, which is computationally feasible and asymptotically valid in the sense that the learned model satisfies the fairness constraint asymptotically and achieves a fast convergence rate. Numerical experiments confirm that the SLIDE works well for various benchmark datasets.

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