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# Approximation Algorithms for Socially Fair Clustering

*Proceedings of Thirty Fourth Conference on Learning Theory*, PMLR 134:3246-3264, 2021.

#### Abstract

We present an (e^{O(p)} (log \ell) / (log log \ell))-approximation algorithm for socially fair clustering with the l_p-objective. In this problem, we are given a set of points in a metric space. Each point belongs to one (or several) of \ell groups. The goal is to find a k-medians, k-means, or, more generally, l_p-clustering that is simultaneously good for all of the groups. More precisely, we need to find a set of k centers C so as to minimize the maximum over all groups j of \sum_{u in group j} d(u, C)^p. The socially fair clustering problem was independently proposed by Abbasi, Bhaskara, and Venkatasubramanian (2021) and Ghadiri, Samadi, and Vempala (2021). Our algorithm improves and generalizes their O(\ell)-approximation algorithms for the problem. The natural LP relaxation for the problem has an integrality gap of \Omega(\ell). In order to obtain our result, we introduce a strengthened LP relaxation and show that it has an integrality gap of \Theta((log \ell) / (log log \ell)) for a fixed p. Additionally, we present a bicriteria approximation algorithm, which generalizes the bicriteria approximation of Abbasi et al. (2021).