FACROC: a fairness measure for FAir Clustering through ROC curves

• URL: http://arxiv.org/abs/2503.00854v1
• Date: Sun, 02 Mar 2025 11:11:34 GMT
• Title: FACROC: a fairness measure for FAir Clustering through ROC curves
• Authors: Tai Le Quy, Long Le Thanh, Lan Luong Thi Hong, Frank Hopfgartner,
• Abstract summary: We introduce a new visual-based fairness measure for fair clustering through ROC curves, namely FACROC.<n>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.
• Score: 1.936466750657896
• License: http://creativecommons.org/licenses/by/4.0/
• 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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