Active learning with fairness-aware clustering for fair classification considering multiple sensitive attributes

Abstract

Fairness concerns have recently been gaining increasing attention in machine learning (ML) research and applications. ML models typically require massive data, which can be costly and challenging for collecting and labeling. To address these concerns, active learning has been proposed as a promising approach to build accurate and fair ML models by interactively querying an oracle within a labeling budget. However, current research on fair active learning only focuses on single sensitive attribute, where the model often show unfairness on subgroups defined by the intersections of different protected attribute values. In this article, a novel active learning approach with a fairness-aware clustering technique is proposed for fair classification that can simultaneously ensure the equality of fairness metrics across multiple sensitive attributes. The fair clustering method enables to control the trade-off levels between the clustering and fairness objectives with multiple sensitive attributes, which mitigates bias and protects the unprivileged classes by balancing their representativeness in all clusters. A tight upper bound is theoretically derived for our fairness objective based on its concave-convex decomposition, which can be jointly optimized with the clustering objective through Majorize-Minimize technique and enable parallel computing. The active learning sampler aims to select highly informative and representative samples from fair clusters to enhance the model classification accuracy and fairness. The effectiveness of our proposed approach is demonstrated by comparing with state-of-the-art methods on real-world datasets. The experimental results are accordant with theoretic analysis, and show that (i) our approach significantly improves the fairness while maintaining model classification accuracy; (ii) our approach outperforms state-of-the-art fair active learning methods across fairness metrics on both single and multiple sensitive attributes for fair classification tasks.