Constant-Factor Approximations for Doubly Constrained Fair k-Center, k-Median and k-Means

TL;DR

This paper introduces new constant-factor approximation algorithms for doubly constrained fair clustering problems, improving guarantees for k-center and providing first such algorithms for k-median and k-means.

Contribution

It presents the first constant-factor approximation algorithms for doubly constrained fair k-median and k-means clustering, and improves guarantees for k-center.

Findings

Achieves a 4-approximation for k-center with small violation.

Provides the first constant-factor algorithms for fair k-median and k-means.

Algorithms are LP-based and generalize to other center-selection constraints.

Abstract

We study discrete k-clustering problems in general metric spaces that are constrained by a combination of two different fairness conditions within the demographic fairness model. Given a metric space (P,d), where every point in P is equipped with a protected attribute, and a number k, the goal is to partition P into k clusters with a designated center each, such that a center-based objective function is minimized and the attributes are fairly distributed with respect to the following two fairness concepts: 1) group fairness: We aim for clusters with balanced numbers of attributes by specifying lower and upper bounds for the desired attribute proportions. 2) diverse center selection: Clusters have natural representatives, i.e., their centers. We ask for a balanced set of representatives by specifying the desired number of centers to choose from each attribute. Dickerson, Esmaeili, Morgenstern and Zhang (2023) denote the combination of these two constraints as doubly constrained fair clustering. They present algorithms whose guarantees depend on the best known approximation factors for either of these problems. Currently, this implies an 8-approximation with a small additive violation on the group fairness constraint. For k-center, we improve this approximation factor to 4 with a small additive violation. This guarantee also depends on the currently best algorithm for DS-fair k-center given by Jones, Nguyen and Nguyen (2020). For k-median and k-means, we propose the first constant-factor approximation algorithms. Our algorithms transform a solution that satisfies diverse center selection into a doubly constrained fair clustering using an LP-based approach. Furthermore, our results are generalizable to other center-selection constraints, such as matroid k-clustering and knapsack constraints.