# FACROC: a fairness measure for FAir Clustering through ROC curves

arXiv: https://arxiv.org/abs/2503.00854 · HTML: https://mlsanity.com/paper/2503.00854/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [Fair k-Center Clustering for Data Summarization](https://mlsanity.com/paper/1901.08628/) (2019)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [A New Notion of Individually Fair Clustering: α-Equitable k-Center](https://mlsanity.com/paper/2106.05423/) (2021)
- [Diversity-aware k-median : Clustering with fair center representation](https://mlsanity.com/paper/2106.11696/) (2021)
- [The Area Under the ROC Curve as a Measure of Clustering Quality](https://mlsanity.com/paper/2009.02400/) (2020)
- [A review of clustering models in educational data science towards fairness-aware learning](https://mlsanity.com/paper/2301.03421/) (2023)

## 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.