用ROC曲线差异衡量聚类公平性，更直观评估不同群体的聚类质量差异。
FACROC: a fairness measure for FAir Clustering through ROC curves
- 基于受试者工作特征曲线（ROC）构建公平性评估方法
- 通过AUCC量化聚类质量，对比不同敏感属性值下的分类性能差异
- 适合关注聚类公平性的研究人员和算法开发者
公平聚类近年来受到广泛关注。尽管已有多种公平性度量方法，但大多未考虑聚类质量与敏感属性取值之间的关系。本文提出一种基于可视化的新公平性度量方法FACROC，利用受试者工作特征曲线（ROC）分析不同敏感属性值下的聚类表现，并以AUCC作为聚类质量指标，计算对应ROC曲线的差异。在多个公平机器学习常用数据集及知名（公平）聚类模型上的实验表明，FACROC能有效实现对聚类公平性的视觉评估，具有良好的实用价值。
原文摘要 · Abstract (English)
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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