Representativity Fairness in Clustering

TL;DR

This paper introduces a new fairness concept called representativity fairness in clustering, aiming to reduce disparities in how objects relate to their cluster centers, and proposes a novel algorithm to optimize this fairness with minimal impact on clustering quality.

Contribution

The paper proposes a new fairness notion in clustering, developes the RFKM algorithm to optimize for this fairness, and demonstrates its effectiveness on public datasets.

Findings

RFKM significantly improves representativity fairness.

RFKM maintains high clustering quality.

Empirical results show effectiveness across datasets.

Abstract

Incorporating fairness constructs into machine learning algorithms is a topic of much societal importance and recent interest. Clustering, a fundamental task in unsupervised learning that manifests across a number of web data scenarios, has also been subject of attention within fair ML research. In this paper, we develop a novel notion of fairness in clustering, called representativity fairness. Representativity fairness is motivated by the need to alleviate disparity across objects' proximity to their assigned cluster representatives, to aid fairer decision making. We illustrate the importance of representativity fairness in real-world decision making scenarios involving clustering and provide ways of quantifying objects' representativity and fairness over it. We develop a new clustering formulation, RFKM, that targets to optimize for representativity fairness along with clustering quality. Inspired by the $K$-Means framework, RFKM incorporates novel loss terms to formulate an objective function. The RFKM objective and optimization approach guides it towards clustering configurations that yield higher representativity fairness. Through an empirical evaluation over a variety of public datasets, we establish the effectiveness of our method. We illustrate that we are able to significantly improve representativity fairness at only marginal impact to clustering quality.