# Towards Fair Deep Clustering With Multi-State Protected Variables

arXiv: https://arxiv.org/abs/1901.10053 · HTML: https://mlsanity.com/paper/1901.10053/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric](https://mlsanity.com/paper/2209.12396/) (2022)
- [Robust Fair Clustering: A Novel Fairness Attack and Defense Framework](https://mlsanity.com/paper/2210.01953/) (2022)
- [Deep Fair Discriminative Clustering](https://mlsanity.com/paper/2105.14146/) (2021)
- [Efficient Algorithms For Fair Clustering with a New Fairness Notion](https://mlsanity.com/paper/2109.00708/) (2021)
- [FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation](https://mlsanity.com/paper/2310.13820/) (2023)
- [FiSH: Fair Spatial Hotspots](https://mlsanity.com/paper/2106.06049/) (2021)
- [From Discrete to Continuous: Deep Fair Clustering With Transferable Representations](https://mlsanity.com/paper/2403.16201/) (2024)
- [Recovering Fairness Directly from Modularity: a New Way for Fair Community Partitioning](https://mlsanity.com/paper/2505.22684/) (2025)
- [UniFair: A unified fair clustering approach based on separation and compactness](https://mlsanity.com/paper/2606.04777/) (2026)

## Abstract (arXiv)

Fair clustering under the disparate impact doctrine requires that population of each protected group should be approximately equal in every cluster. Previous work investigated a difficult-to-scale pre-processing step for k-center and k-median style algorithms for the special case of this problem when the number of protected groups is two. In this work, we consider a more general and practical setting where there can be many protected groups. To this end, we propose Deep Fair Clustering, which learns a discriminative but fair cluster assignment function. The experimental results on three public datasets with different types of protected attribute show that our approach can steadily improve the degree of fairness while only having minor loss in terms of clustering quality.