Achieving Downstream Fairness with Geometric Repair

Abstract

Consider a scenario where some "upstream" model developer must train a fair model, but is unaware of the fairness requirements of a "downstream" model user/stakeholder. In the context of fair classification, we present a technique that specifically addresses this setting, by post-processing a regressor's scores such they yield fair classifications for any downstream choice in decision threshold. To begin, we leverage ideas from optimal transport to show how this can be achieved for binary protected groups across a broad class of fairness metrics. Then, we extend our approach to address the setting where a protected attribute takes on multiple values, by re-recasting our technique as a convex optimization problem that leverages lexicographic fairness.