Fair Clustering: A Causal Perspective

Clustering algorithms may unintentionally propagate or intensify existing disparities, leading to unfair representations or biased decision-making. Current fair clustering methods rely on notions of fairness that do not capture any information on the underlying causal mechanisms. We show that optimi...

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Bibliographic Details
Main Authors Bayer, Fritz, Plecko, Drago, Beerenwinkel, Niko, Kuipers, Jack
Format Journal Article
LanguageEnglish
Published 14.12.2023
Subjects
Computer Science - Computers and Society
Computer Science - Learning
Statistics - Machine Learning
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Summary:Clustering algorithms may unintentionally propagate or intensify existing disparities, leading to unfair representations or biased decision-making. Current fair clustering methods rely on notions of fairness that do not capture any information on the underlying causal mechanisms. We show that optimising for non-causal fairness notions can paradoxically induce direct discriminatory effects from a causal standpoint. We present a clustering approach that incorporates causal fairness metrics to provide a more nuanced approach to fairness in unsupervised learning. Our approach enables the specification of the causal fairness metrics that should be minimised. We demonstrate the efficacy of our methodology using datasets known to harbour unfair biases.
DOI:10.48550/arxiv.2312.09061