Fair Clustering: Critique, Caveats, and Future Directions

TL;DR

This paper critically examines fair clustering, highlighting overlooked issues like utility measurement and downstream impacts, and suggests future research directions to improve its societal and practical effectiveness.

Contribution

It provides a critical analysis of fair clustering, identifying key gaps such as utility characterization and social impact considerations, and proposes steps for more impactful future research.

Findings

Identifies lack of clear utility metrics in fair clustering

Highlights potential negative social impacts of fair clustering algorithms

Suggests research directions for more socially beneficial fair clustering

Abstract

Clustering is a fundamental problem in machine learning and operations research. Therefore, given the fact that fairness considerations have become of paramount importance in algorithm design, fairness in clustering has received significant attention from the research community. The literature on fair clustering has resulted in a collection of interesting fairness notions and elaborate algorithms. In this paper, we take a critical view of fair clustering, identifying a collection of ignored issues such as the lack of a clear utility characterization and the difficulty in accounting for the downstream effects of a fair clustering algorithm in machine learning settings. In some cases, we demonstrate examples where the application of a fair clustering algorithm can have significant negative impacts on social welfare. We end by identifying a collection of steps that would lead towards more impactful research in fair clustering.