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arXiv · 2503.00854

FACROC: a fairness measure for FAir Clustering through ROC curves

Tai Le Quy·Long Le Thanh·Lan Luong Thi Hong·Frank Hopfgartner
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Abstract

Fair clustering has attracted remarkable attention from the research community. Many fairness measures for clustering have been proposed; however, they do not take into account the clustering quality w.r.t. the values of the protected attribute. In this paper, we introduce a new visual-based fairness measure for fair clustering through ROC curves, namely FACROC. This fairness measure employs AUCC as a measure of clustering quality and then computes the difference in the corresponding ROC curves for each value of the protected attribute. Experimental results on several popular datasets for fairness-aware machine learning and well-known (fair) clustering models show that FACROC is a beneficial method for visually evaluating the fairness of clustering models.

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Tai Le Quy, Long Le Thanh, Lan Luong Thi Hong, Frank Hopfgartner. 2025-03-02. FACROC: a fairness measure for FAir Clustering through ROC curves. https://doi.org/10.1007/978-981-96-8295-9_25

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