Whither Fair Clustering?
This is an incremental position paper that aims to expand research in fair clustering, which is important for improving fairness in unsupervised learning applications affecting human lives.
The paper critiques the current state of fair clustering research, identifying unexplored directions and advocating for broadening normative principles, addressing shortfalls, and incorporating downstream knowledge to enhance its scope.
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.