Auditing and Enforcing Conditional Fairness via Optimal Transport

TL;DR

This paper introduces new optimal transport-based measures and methods to audit and enforce conditional demographic parity in predictive models, especially when conditioning variables are complex or continuous.

Contribution

It proposes novel CDD measures and regularization methods, airbit{} and airlp{}, to achieve conditional demographic parity in challenging scenarios.

Findings

Effective in handling high-level conditioning variables

Achieves full distributional equality for continuous outputs

Validated on real-world datasets

Abstract

Conditional demographic parity (CDP) is a measure of the demographic parity of a predictive model or decision process when conditioning on an additional feature or set of features. Many algorithmic fairness techniques exist to target demographic parity, but CDP is much harder to achieve, particularly when the conditioning variable has many levels and/or when the model outputs are continuous. The problem of auditing and enforcing CDP is understudied in the literature. In light of this, we propose novel measures of {conditional demographic disparity (CDD)} which rely on statistical distances borrowed from the optimal transport literature. We further design and evaluate regularization-based approaches based on these CDD measures. Our methods, \fairbit{} and \fairlp{}, allow us to target CDP even when the conditioning variable has many levels. When model outputs are continuous, our methods target full equality of the conditional distributions, unlike other methods that only consider first moments or related proxy quantities. We validate the efficacy of our approaches on real-world datasets.