Socially Fair k-Means Clustering

• URL: http://arxiv.org/abs/2006.10085v2
• Date: Thu, 29 Oct 2020 16:03:50 GMT
• Title: Socially Fair k-Means Clustering
• Authors: Mehrdad Ghadiri, Samira Samadi, Santosh Vempala
• Abstract summary: We present a fair k-means objective and algorithm to choose cluster centers that provide equitable costs for different groups. The algorithm, Fair-Lloyd, is a modification of Lloyd's for k-means, inheriting its simplicity, efficiency, and stability.
• Score: 3.3409719900340256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups). Such biased clusterings can have deleterious implications for human-centric applications such as resource allocation. We present a fair k-means objective and algorithm to choose cluster centers that provide equitable costs for different groups. The algorithm, Fair-Lloyd, is a modification of Lloyd's heuristic for k-means, inheriting its simplicity, efficiency, and stability. In comparison with standard Lloyd's, we find that on benchmark datasets, Fair-Lloyd exhibits unbiased performance by ensuring that all groups have equal costs in the output k-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever k-means is currently used.

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