2019
Fair Algorithms for Clustering
NeurIPS 2019poster
We study the problem of finding low-cost {\em fair clusterings} in data where each data point may belong to many protected groups. Our work significantly generalizes the seminal work of Chierichetti \etal (NIPS 2017) as follows. - We allow the user to specify the parameters that define fair repr…