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Proportionally Fair Clustering

9 May 2019
Xingyu Chen
Brandon Fain
Charles Lyu
Kamesh Munagala
    FedMLFaML
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Abstract

We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context. For clustering nnn points with kkk centers, we define fairness as proportionality to mean that any n/kn/kn/k points are entitled to form their own cluster if there is another center that is closer in distance for all n/kn/kn/k points. We seek clustering solutions to which there are no such justified complaints from any subsets of agents, without assuming any a priori notion of protected subsets. We present and analyze algorithms to efficiently compute, optimize, and audit proportional solutions. We conclude with an empirical examination of the tradeoff between proportional solutions and the kkk-means objective.

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