Beyond Adult and COMPAS: Fairness in Multi-Class Prediction

TL;DR

This paper introduces a scalable post-processing method for fair multi-class probabilistic classifiers that projects pre-trained models onto fairness constraints, balancing accuracy and fairness effectively.

Contribution

It proposes a novel multiplicative post-processing approach with theoretical guarantees and demonstrates competitive performance on large, complex datasets.

Findings

Maintains accuracy-fairness trade-offs comparable to state-of-the-art methods

Offers a parallelizable algorithm with convergence guarantees

Performs efficiently on datasets with over 1 million samples

Abstract

We consider the problem of producing fair probabilistic classifiers for multi-class classification tasks. We formulate this problem in terms of "projecting" a pre-trained (and potentially unfair) classifier onto the set of models that satisfy target group-fairness requirements. The new, projected model is given by post-processing the outputs of the pre-trained classifier by a multiplicative factor. We provide a parallelizable iterative algorithm for computing the projected classifier and derive both sample complexity and convergence guarantees. Comprehensive numerical comparisons with state-of-the-art benchmarks demonstrate that our approach maintains competitive performance in terms of accuracy-fairness trade-off curves, while achieving favorable runtime on large datasets. We also evaluate our method at scale on an open dataset with multiple classes, multiple intersectional protected groups, and over 1M samples.