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
Published inarXiv.org
Main Authors Bayer, Fritz, Drago Plecko, Beerenwinkel, Niko, Kuipers, Jack
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.12.2023
Subjects
Algorithms
Clustering
Unsupervised 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.
ISSN:2331-8422