  A clustering may be considered as fair on pre-specified sensitive attributes
if the proportions of sensitive attribute groups in each cluster reflect that
in the dataset. In this paper, we consider the task of fair clustering for
scenarios involving multiple multi-valued or numeric sensitive attributes. We
propose a fair clustering method, \textit{FairKM} (Fair K-Means), that is
inspired by the popular K-Means clustering formulation. We outline a
computational notion of fairness which is used along with a cluster coherence
objective, to yield the FairKM clustering method. We empirically evaluate our
approach, wherein we quantify both the quality and fairness of clusters, over
real-world datasets. Our experimental evaluation illustrates that the clusters
generated by FairKM fare significantly better on both clustering quality and
fair representation of sensitive attribute groups compared to the clusters from
a state-of-the-art baseline fair clustering method.
