An Empirical Study of Rich Subgroup Fairness for Machine Learning

TL;DR

This paper empirically evaluates Kearns et al.'s rich subgroup fairness algorithm, demonstrating its quick convergence, fairness-accuracy tradeoffs, and practical viability on real datasets.

Contribution

It provides an extensive empirical analysis of the rich subgroup fairness algorithm, comparing it with marginal fairness approaches and assessing its real-world effectiveness.

Findings

The algorithm converges quickly in practice.

Large fairness gains are achievable with mild accuracy costs.

Marginal fairness often results in subgroup unfairness.

Abstract

Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across protected groups), but then asks that this constraint hold over an exponentially or infinitely large collection of subgroups defined by a class of functions with bounded VC dimension. They give an algorithm guaranteed to learn subject to this constraint, under the condition that it has access to oracles for perfectly learning absent a fairness constraint. In this paper, we undertake an extensive empirical evaluation of the algorithm of Kearns et al. On four real datasets for which fairness is a concern, we investigate the basic convergence of the algorithm when instantiated with fast heuristics in place of learning oracles, measure the tradeoffs between fairness and accuracy, and compare this approach with the recent algorithm of Agarwal et al. [2018], which implements weaker and more traditional marginal fairness constraints defined by individual protected attributes. We find that in general, the Kearns et al. algorithm converges quickly, large gains in fairness can be obtained with mild costs to accuracy, and that optimizing accuracy subject only to marginal fairness leads to classifiers with substantial subgroup unfairness. We also provide a number of analyses and visualizations of the dynamics and behavior of the Kearns et al. algorithm. Overall we find this algorithm to be effective on real data, and rich subgroup fairness to be a viable notion in practice.