Fair Clustering Through Fairlets

NeurIPS 2017 Flavio ChierichettiRavi KumarSilvio LattanziSergei Vassilvitskii

We study the question of fair clustering under the {\em disparate impact} doctrine, where each protected class must have approximately equal representation in every cluster. We formulate the fair clustering problem under both the $k$-center and the $k$-median objectives, and show that even with two protected classes the problem is challenging, as the optimum solution can violate common conventions---for instance a point may no longer be assigned to its nearest cluster center!.. (read more)

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