Related papers: Fair Hierarchical Clustering

Fair Hierarchical Clustering

URL: http://arxiv.org/abs/2006.10221v2
Date: Fri, 19 Jun 2020 02:59:47 GMT
Title: Fair Hierarchical Clustering
Authors: Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang
Abstract summary: We define a notion of fairness that mitigates over-representation in traditional clustering. We show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.
Score: 92.03780518164108
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.

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