  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 hiding sensitive
information at test time or have a simple and fast implementation. We show
empirical performance against different fairness baselines on several benchmark
fairness datasets.
