  In this paper, we cast fair machine learning as invariant machine learning.
We first formulate a version of individual fairness that enforces invariance on
certain sensitive sets. We then design a transport-based regularizer that
enforces this version of individual fairness and develop an algorithm to
minimize the regularizer efficiently. Our theoretical results guarantee the
proposed approach trains certifiably fair ML models. Finally, in the
experimental studies we demonstrate improved fairness metrics in comparison to
several recent fair training procedures on three ML tasks that are susceptible
to algorithmic bias.

Machine Learning;Machine Learning