FACROC: a fairness measure for FAir Clustering through ROC curves

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.

Accepted to Special Session: Data Science: Foundations and Applications (DSFA), PAKDD 2025

References in corpus (3)

• Clustering without Over-Representation
• The Area Under the ROC Curve as a Measure of Clustering Quality
• A review of clustering models in educational data science towards fairness-aware learning