FACROC: a fairness measure for FAir Clustering through ROC curves
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908429131448320 |
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| author | Quy, Tai Le Thanh, Long Le Hong, Lan Luong Thi Hopfgartner, Frank |
| author_facet | Quy, Tai Le Thanh, Long Le Hong, Lan Luong Thi Hopfgartner, Frank |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_00854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FACROC: a fairness measure for FAir Clustering through ROC curves Quy, Tai Le Thanh, Long Le Hong, Lan Luong Thi Hopfgartner, Frank Machine Learning 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. |
| title | FACROC: a fairness measure for FAir Clustering through ROC curves |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2503.00854 |