Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection

Wael Alghamdi, Hsiang Hsu, Haewon Jeong, Hao Wang, Peter Winston Michalak, Shahab Asoodeh, Flavio Calmon

Keywords: group fairness, information projection, multi-class classification, new dataset

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 groups, and over 1M samples.

TL;DR: We introduce a post-processing fairness intervention for multi-class probabilistic classifiers.