Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes

TL;DR

This paper introduces FairCOCCO, a novel fairness measure and regularisation method for machine learning models that protect multiple sensitive attributes of various types, improving fairness without sacrificing predictive accuracy.

Contribution

The paper presents FairCOCCO, a new fairness measure and regularisation approach that handles multiple, non-binary sensitive attributes, filling a key gap in fair ML research.

Findings

FairCOCCO effectively quantifies fairness across multiple attribute types.

The regularisation improves fairness while maintaining predictive performance.

Empirical results outperform existing fairness techniques on real datasets.

Abstract

It is important to guarantee that machine learning algorithms deployed in the real world do not result in unfairness or unintended social consequences. Fair ML has largely focused on the protection of single attributes in the simpler setting where both attributes and target outcomes are binary. However, the practical application in many a real-world problem entails the simultaneous protection of multiple sensitive attributes, which are often not simply binary, but continuous or categorical. To address this more challenging task, we introduce FairCOCCO, a fairness measure built on cross-covariance operators on reproducing kernel Hilbert Spaces. This leads to two practical tools: first, the FairCOCCO Score, a normalised metric that can quantify fairness in settings with single or multiple sensitive attributes of arbitrary type; and second, a subsequent regularisation term that can be incorporated into arbitrary learning objectives to obtain fair predictors. These contributions address crucial gaps in the algorithmic fairness literature, and we empirically demonstrate consistent improvements against state-of-the-art techniques in balancing predictive power and fairness on real-world datasets.