FACROC: a fairness measure for FAir Clustering through ROC curves
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Abstract (arXiv)
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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References · 11
- 2018Fair Clustering Through Fairletscited by 176
- 2021A survey on datasets for fairness-aware machine learningcited by 157
- 2019Fair Algorithms for Clusteringcited by 98
- 2019Scalable Fair Clusteringcited by 78
- 2019Proportionally Fair Clusteringcited by 69
- 2019Fair k-Center Clustering for Data Summarizationcited by 65
- 2019Clustering without Over-Representationcited by 50
- 2021A New Notion of Individually Fair Clustering: α-Equitable k-Centercited by 18
- 2021Diversity-aware k-median : Clustering with fair center representationcited by 16
- 2020The Area Under the ROC Curve as a Measure of Clustering Qualitycited by 8
- 2023A review of clustering models in educational data science towards fairness-aware learningcited by 3
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