Efficient Algorithms For Fair Clustering with a New Fairness Notion

TL;DR

This paper introduces a new fairness notion called tau-fairness for clustering, providing a scalable algorithm that balances fairness and clustering quality more effectively than existing methods.

Contribution

It proposes tau-fairness, a flexible fairness measure, and demonstrates that simple greedy algorithms can efficiently achieve a better fairness-quality trade-off.

Findings

Outperforms state-of-the-art algorithms in experiments.

Efficient greedy algorithms achieve the fairness-quality trade-off.

Works well even with many clusters.

Abstract

We revisit the problem of fair clustering, first introduced by Chierichetti et al., that requires each protected attribute to have approximately equal representation in every cluster; i.e., a balance property. Existing solutions to fair clustering are either not scalable or do not achieve an optimal trade-off between clustering objective and fairness. In this paper, we propose a new notion of fairness, which we call $tau$-fair fairness, that strictly generalizes the balance property and enables a fine-grained efficiency vs. fairness trade-off. Furthermore, we show that simple greedy round-robin based algorithms achieve this trade-off efficiently. Under a more general setting of multi-valued protected attributes, we rigorously analyze the theoretical properties of the our algorithms. Our experimental results suggest that the proposed solution outperforms all the state-of-the-art algorithms and works exceptionally well even for a large number of clusters.