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arXiv · 2006.10221

Fair Hierarchical Clustering

Sara Ahmadian·Alessandro Epasto·Marina Knittel·Ravi Kumar·Mohammad Mahdian·Benjamin Moseley·Philip Pham·Sergei Vassilvitskii·Yuyan Wang
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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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Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang. 2020-06-18. Fair Hierarchical Clustering. https://arxiv.org/abs/2006.10221

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