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Poster
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
Henry C Bendekgey · Erik Sudderth

Thu Dec 09 04:30 PM -- 06:00 PM (PST) @
in Poster Session 7 »

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 surrogates perform comparably to the state-of-the-art on low-dimensional fairness benchmarks, while achieving superior accuracy and stability for more complex computer vision and natural language processing tasks.

Keywords: Deep Learning · Optimization · Machine Learning · Vision · Fairness · Language

Author Information

Henry C Bendekgey (UC Irvine)
Erik Sudderth (Brown University)

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