FACROC: a fairness measure for FAir Clustering through ROC curves

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.

@article{quy2025_2503.00854,
  title={ FACROC: a fairness measure for FAir Clustering through ROC curves },
  author={ Tai Le Quy and Long Le Thanh and Lan Luong Thi Hong and Frank Hopfgartner },
  journal={arXiv preprint arXiv:2503.00854},
  year={ 2025 }
}