Fair Clustering via Hierarchical Fair-Dirichlet Process

2023-05-27 · Abhisek Chakraborty, Anirban Bhattacharya, Debdeep Pati

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.

📄 PDF Abstract BibTeX arXiv:2305.17557

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AttributeClusteringDecision Making Fairness

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