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Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
2021
Neural Information Processing Systems
We investigate how fairness relaxations scale to flexible classifiers like deep neural networks for images and text. We analyze an easy-to-use and robust way of imposing fairness constraints when training, and through this framework prove that some prior fairness surrogates exhibit degeneracies for non-convex models. We resolve these problems via three new surrogates: an adaptive data re-weighting, and two smooth upper-bounds that are provably more robust than some previous methods. Our
dblp:conf/nips/BendekgeyS21
fatcat:ukaat4qojfaurpncmd5nwaax5i