# Whither Fair Clustering?

arXiv: https://arxiv.org/abs/2007.07838 · HTML: https://mlsanity.com/paper/2007.07838/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Proportionally Representative Clustering](https://mlsanity.com/paper/2304.13917/) (2023)
- [Efficient Algorithms For Fair Clustering with a New Fairness Notion](https://mlsanity.com/paper/2109.00708/) (2021)
- [Fair Labeled Clustering](https://mlsanity.com/paper/2205.14358/) (2022)
- [Cluster-level Group Representativity Fairness in k-means Clustering](https://mlsanity.com/paper/2212.14467/) (2022)

## Abstract (arXiv)

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.