Practical Approaches for Fair Learning with Multitype and Multivariate Sensitive Attributes
This addresses fairness issues in practical ML applications where multiple sensitive attributes are involved, offering tools for broader real-world deployment, though it builds incrementally on existing fairness methods.
The authors tackled the challenge of ensuring fairness in machine learning when multiple sensitive attributes of various types (continuous, categorical) need protection, introducing FairCOCCO, a fairness measure based on cross-covariance operators, which led to a metric and regularization method that empirically improved predictive power and fairness on real-world datasets.
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.