PRACTICAL APPROACHES FOR FAIR LEARNING WITH MULTITYPE AND MULTIVARIATE SENSITIVE ATTRIBUTES Anonymous

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 normalized metric that can quantify fairness in settings with single or multiple sensitive attributes of arbitrary type; and second, a subsequent regularization 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.

1. INTRODUCTION

There is a clear need for scalable and practical methods that can be easily incorporated into machine learning (ML) operations, in order to make sure they don't inadvertently disadvantage one group over another. The ML community has responded with a number of methods designed to ensure that predictive models are fair (under a variety of definitions that we shall explore later) (Caton & Haas, 2020) . Perhaps due to the archetypal fairness example, an investigation into the COMPAS software that found racial discrimination in the assessment of risk of recidivism (Angwin et al., 2016) , most of the focus has been on single, binary variables -in this case race being treated as an indicator of whether an individual was black or white. This, combined with a discrete target, allows for easy analysis of fairness criteria such as demographic parity and equalized odds (Barocas & Selbst, 2016; Hardt et al., 2016) , through the rates of outcomes in the confusion matrix of the subgroups. The problem is, however, that in many practical applications we may have multiple attributes which we would like to protect, for example both race and sex -indeed U.S. federal law protects groups from discrimination based on nine protected classes (EEOC, 2021). Algorithms deployed in the real-world therefore need to be capable of protecting multiple attributes both jointly (e.g. 'black woman') and individually (e.g. 'black' and 'woman'). This is non-trivial and cannot be simply achieved by introducing separate fairness conditions for each attribute. Such an approach both does not provide joint protection of sensitive attributes and complicates matters by introducing additional hyperparameters that need to be traded-off against each other. Matters are further complicated by the fact that many sensitive attributes (e.g. age) and outcomes (e.g. credit limit) take on continuous values, for which calculated rates do not make sense. Existing methods simply discretise these into categorical bins, which leads to several issues in practice, as it entails thresholding and data sparsity effects while discarding element order information. As we shall see later in Section 4, this approach is unlikely to be optimal in delivering discriminative yet fair predictors. Contributions and Outline. Consequently, we introduce two practical tools to the community, which we hope can be used to more easily incorporate fairness into a standard ML pipeline: a (1) Fairness

