Feature-based Individual Fairness in k-clustering

Published: 01 Jan 2023, Last Modified: 13 May 2025AAMAS 2023EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: Ensuring fairness in machine learning algorithms is a challenging and essential task. We consider the problem of clustering a set of points while satisfying fairness constraints. While there have been several attempts to capture group fairness in the k-clustering problem, fairness at an individual level is not so well-studied. We introduce a new notion of individual fairness in k-clustering based on features not necessarily used for clustering. The problem is NP-hard and does not admit a constant factor approximation. Therefore, we design a randomized heuristic algorithm. Our experimental results against six competing baselines validate that our algorithm produces individually fairer clusters than the fairest baseline.
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