Fair and Optimal Classification via Post-Processing

TL;DR

This paper characterizes the fundamental tradeoff between fairness and accuracy in classification, providing a Wasserstein-barycenter framework and a practical post-processing algorithm that achieves optimal fairness-accuracy balance.

Contribution

It offers a complete theoretical characterization of demographic parity tradeoffs and introduces a simple, effective post-processing method for fair classification.

Findings

Optimal error rate characterized by Wasserstein-barycenter

Proposed post-processing algorithm achieves optimal fairness

Effective on benchmark datasets

Abstract

To mitigate the bias exhibited by machine learning models, fairness criteria can be integrated into the training process to ensure fair treatment across all demographics, but it often comes at the expense of model performance. Understanding such tradeoffs, therefore, underlies the design of fair algorithms. To this end, this paper provides a complete characterization of the inherent tradeoff of demographic parity on classification problems, under the most general multi-group, multi-class, and noisy setting. Specifically, we show that the minimum error rate achievable by randomized and attribute-aware fair classifiers is given by the optimal value of a Wasserstein-barycenter problem. On the practical side, our findings lead to a simple post-processing algorithm that derives fair classifiers from score functions, which yields the optimal fair classifier when the score is Bayes optimal. We provide suboptimality analysis and sample complexity for our algorithm, and demonstrate its effectiveness on benchmark datasets.