Wasserstein Fair Classification

We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictions from model outputs.We introduce different methods that enable hid-ing sensitive information at test time or have a simple and fast implementation. We show empirical performance against different fair-ness baselines on several benchmark fairness datasets.