# Fairness in Clustering with Multiple Sensitive Attributes

arXiv: https://arxiv.org/abs/1910.05113 · HTML: https://mlsanity.com/paper/1910.05113/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 21 of 21)

- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [Approximation Algorithms for Socially Fair Clustering](https://mlsanity.com/paper/2103.02512/) (2021)
- [A Stochastic Alternating Balance k-Means Algorithm for Fair Clustering](https://mlsanity.com/paper/2105.14172/) (2021)
- [The Eye of Sherlock Holmes: Uncovering User Private Attribute Profiling via Vision-Language Model Agentic Framework](https://mlsanity.com/paper/2505.19139/) (2025)
- [Constant-Factor Approximation Algorithms for Socially Fair k-Clustering](https://mlsanity.com/paper/2206.11210/) (2022)
- [FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering](https://mlsanity.com/paper/2209.12756/) (2022)
- [Efficient Algorithms For Fair Clustering with a New Fairness Notion](https://mlsanity.com/paper/2109.00708/) (2021)
- [Fairness Degrading Adversarial Attacks Against Clustering Algorithms](https://mlsanity.com/paper/2110.12020/) (2021)
- [Fairness through Social Welfare Optimization](https://mlsanity.com/paper/2102.00311/) (2021)
- [A review of clustering models in educational data science towards fairness-aware learning](https://mlsanity.com/paper/2301.03421/) (2023)
- [Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation](https://mlsanity.com/paper/2509.08163/) (2025)
- [FiSH: Fair Spatial Hotspots](https://mlsanity.com/paper/2106.06049/) (2021)
- [Increasing Fairness in Predictions Using Bias Parity Score Based Loss Function Regularization](https://mlsanity.com/paper/2111.03638/) (2021)
- [Exploring Rawlsian Fairness for K-Means Clustering](https://mlsanity.com/paper/2205.02052/) (2022)
- [Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina](https://mlsanity.com/paper/2205.14867/) (2022)
- [Cluster-level Group Representativity Fairness in k-means Clustering](https://mlsanity.com/paper/2212.14467/) (2022)
- [Fair Clustering with Clusterlets](https://mlsanity.com/paper/2505.06259/) (2025)
- [Fast and effective algorithms for fair clustering at scale](https://mlsanity.com/paper/2605.13759/) (2026)
- [FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Data](https://mlsanity.com/paper/2606.23515/) (2026)
- [COVAriance-Induced Fairness Gap Penalty for Subgroup-Fair Clustering](https://mlsanity.com/paper/2607.18119/) (2026)
- [Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation](https://mlsanity.com/paper/2610.19252/) (2026)

## Abstract (arXiv)

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, textitFairKM (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.