FACROC: A fairness measure for fair clustering through ROC curves

Quy, T.L., Thanh, L.L., Hong, L.L.T. et al. (1 more author) (2025) FACROC: A fairness measure for fair clustering through ROC curves. In: Wu, X., Spiliopoulou, M., Wang, C., Kumar, V., Cao, L., Zhou, X., Pang, G. and Gama, J., (eds.) Data Science: Foundations and Applications: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VI. 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, 10-13 Jun 2025, Sydney, NSW, Australia. Lecture Notes in Computer Science (LNAI 15875). Springer , Singapore , pp. 340-352. ISBN: 9789819682942 ISSN: 0302-9743 EISSN: 1611-3349

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.

Metadata

Item Type:	Proceedings Paper
Authors/Creators:	Quy, T.L. Thanh, L.L. Hong, L.L.T. Hopfgartner, F. https://orcid.org/0000-0003-0380-6088
Editors:	Wu, X. Spiliopoulou, M. Wang, C. Kumar, V. Cao, L. Zhou, X. Pang, G. Gama, J.
Copyright, Publisher and Additional Information:	© 2025 The Author(s). Except as otherwise noted, this author-accepted version of a journal article published in Data Science: Foundations and Applications: 29th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2025, Sydney, NSW, Australia, June 10-13, 2025, Proceedings, Part VI is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Dates:	Published (online): 20 June 2025 Published: 20 June 2025
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User:	Symplectic Sheffield
Date Deposited:	18 Aug 2025 15:27
Last Modified:	18 Aug 2025 15:27
Status:	Published
Publisher:	Springer
Series Name:	Lecture Notes in Computer Science
Refereed:	Yes
Identification Number:	10.1007/978-981-96-8295-9_25
Related URLs:	Conference
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:230461

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FACROC: A fairness measure for fair clustering through ROC curves

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