A Distributionally Robust Approach to Fair Classification

TL;DR

This paper introduces a distributionally robust logistic regression model that incorporates fairness constraints using Wasserstein balls, improving fairness with minimal accuracy loss and providing confidence bounds on unfairness levels.

Contribution

It presents a novel convex optimization framework for fair classification using Wasserstein-based distributional robustness and introduces confidence bounds for unfairness assessment.

Findings

Improves fairness with minimal accuracy loss on synthetic and real data

Provides linear programming bounds on classifier unfairness

Models distributional uncertainty with Wasserstein balls

Abstract

We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity. This model is equivalent to a tractable convex optimization problem if a Wasserstein ball centered at the empirical distribution on the training data is used to model distributional uncertainty and if a new convex unfairness measure is used to incentivize equalized opportunities. We demonstrate that the resulting classifier improves fairness at a marginal loss of predictive accuracy on both synthetic and real datasets. We also derive linear programming-based confidence bounds on the level of unfairness of any pre-trained classifier by leveraging techniques from optimal uncertainty quantification over Wasserstein balls.