Fair Clustering via Hierarchical Fair-Dirichlet Process

Abhisek Chakraborty; Anirban Bhattacharya; Debdeep Pati

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arXiv · 2305.17557

Fair Clustering via Hierarchical Fair-Dirichlet Process

Abhisek Chakraborty·Anirban Bhattacharya·Debdeep Pati

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Abstract

The advent of ML-driven decision-making and policy formation has led to an increasing focus on algorithmic fairness. As clustering is one of the most commonly used unsupervised machine learning approaches, there has naturally been a proliferation of literature on {\em fair clustering}. A popular notion of fairness in clustering mandates the clusters to be {\em balanced}, i.e., each level of a protected attribute must be approximately equally represented in each cluster. Building upon the original framework, this literature has rapidly expanded in various aspects. In this article, we offer a novel model-based formulation of fair clustering, complementing the existing literature which is almost exclusively based on optimizing appropriate objective functions.

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Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati. 2023-05-27. Fair Clustering via Hierarchical Fair-Dirichlet Process. https://arxiv.org/abs/2305.17557

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