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A New Notion of Individually Fair Clustering: α-Equitable k-Center
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:6387-6408, 2022.
Abstract
Clustering is a fundamental problem in unsupervised machine learning, and due to its numerous societal implications fair variants of it have recently received significant attention. In this work we introduce a novel definition of individual fairness for clustering problems. Specifically, in our model, each point j has a set of other points \mathcal{S}_j that it perceives as similar to itself, and it feels that it is being fairly treated if the quality of service it receives in the solution is \alpha-close (in a multiplicative sense, for some given \alpha \geq 1) to that of the points in \mathcal{S}_j. We begin our study by answering questions regarding the combinatorial structure of the problem, namely for what values of \alpha the problem is well-defined, and what the behavior of the Price of Fairness (PoF) for it is. For the well-defined region of \alpha, we provide efficient and easily-implementable approximation algorithms for the k-center objective, which in certain cases also enjoy bounded-PoF guarantees. We finally complement our analysis by an extensive suite of experiments that validates the effectiveness of our theoretical results.