We extend the fair machine learning literature by considering the problem of proportional centroid clustering in a metric context. For clustering n points with k centers, we define fairness as proportionality to mean that any n/k points are entitled to form their own cluster if there is another center that is closer in distance for all n/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 k-means objective.
Xingyu Chen (Duke University)
Brandon Fain (Duke University)
Liang Lyu (Duke University)
Kamesh Munagala (Duke University)
Related Events (a corresponding poster, oral, or spotlight)
2019 Poster: Proportionally Fair Clustering »
Fri Jun 14th 01:30 -- 04:00 AM Room Pacific Ballroom