title: Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
abstract: 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. 
  
  
authors: Harry Bendekgey Erik B. Sudderth
hIndex: Erik B. Sudderth 36