Robust Optimization of Fair Machine Learning Objectives
The core message of this paper is to derive robust variants of fair objectives, such as utilitarian, Gini, and power-mean welfare concepts, by constructing a hierarchy of Rawlsian games where a Dæmon creates a world and an adversarial Angel places the Dæmon within it. These robust fair objectives can be efficiently optimized under mild conditions.