  Within the relatively busy area of fair machine learning that has been
dominated by classification fairness research, fairness in clustering has
started to see some recent attention. In this position paper, we assess the
existing work in fair clustering and observe that there are several directions
that are yet to be explored, and postulate that the state-of-the-art in fair
clustering has been quite parochial in outlook. We posit that widening the
normative principles to target for, characterizing shortfalls where the target
cannot be achieved fully, and making use of knowledge of downstream processes
can significantly widen the scope of research in fair clustering research. At a
time when clustering and unsupervised learning are being increasingly used to
make and influence decisions that matter significantly to human lives, we
believe that widening the ambit of fair clustering is of immense significance.
