论文标题
公平的分层聚类
Fair Hierarchical Clustering
论文作者
论文摘要
随着机器学习变得越来越普遍，研究人员已经开始认识到确保机器学习系统公平的必要性。最近，人们有兴趣定义公平的概念，从而减轻了传统聚类中的过分陈述。 在本文中，我们将此概念扩展到分层聚类，目标是递归分区数据以优化特定目标。对于各种自然目标，我们获得了简单，有效的算法，以找到可证明的公平分层聚类。从经验上讲，我们表明我们的算法可以找到一个公平的等级聚类，而目标的损失只有可忽略的损失。
As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering. In this paper we extend this notion to hierarchical clustering, where the goal is to recursively partition the data to optimize a specific objective. For various natural objectives, we obtain simple, efficient algorithms to find a provably good fair hierarchical clustering. Empirically, we show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.
