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A clustering may be considered as fair on pre-specified sensitive attributesif the proportions of sensitive attribute groups in each cluster reflect thatin the dataset. In this paper, we consider the task of fair clustering forscenarios involving multiple multi-valued or numeric sensitive attributes. Wepropose a fair clustering method, \textit{FairKM} (Fair K-Means), that isinspired by the popular K-Means clustering formulation. We outline acomputational notion of fairness which is used along with a cluster coherenceobjective, to yield the FairKM clustering method. We empirically evaluate ourapproach, wherein we quantify both the quality and fairness of clusters, overreal-world datasets. Our experimental evaluation illustrates that the clustersgenerated by FairKM fare significantly better on both clustering quality andfair representation of sensitive attribute groups compared to the clusters froma state-of-the-art baseline fair clustering method.

Fairness in Clustering with Multiple Sensitive Attributes