Group-blind optimal transport to group parity and its constrained variants

TL;DR

This paper introduces a novel group-blind optimal transport method that achieves demographic group parity in machine learning without needing individual protected attribute values, relying only on group distribution data.

Contribution

It proposes a single group-blind projection map for fair learning that does not require protected attribute values at the individual level, using only distributional information.

Findings

Effective on synthetic data

Demonstrates fairness in real data

Requires only distributional group data

Abstract

Fairness holds a pivotal role in the realm of machine learning, particularly when it comes to addressing groups categorised by protected attributes, e.g., gender, race. Prevailing algorithms in fair learning predominantly hinge on accessibility or estimations of these protected attributes, at least in the training process. We design a single group-blind projection map that aligns the feature distributions of both groups in the source data, achieving (demographic) group parity, without requiring values of the protected attribute for individual samples in the computation of the map, as well as its use. Instead, our approach utilises the feature distributions of the privileged and unprivileged groups in a boarder population and the essential assumption that the source data are unbiased representation of the population. We present numerical results on synthetic data and real data.