# Welfare-Centric Clustering

arXiv: https://arxiv.org/abs/2508.10345 · HTML: https://mlsanity.com/paper/2508.10345/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 45 references that are in the map.
- First posted on arXiv 14 Aug 2025.

## Builds on (most cited first, 30 of 30)

- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [Fair Clustering Through Fairlets](https://mlsanity.com/paper/1802.05733/) (2018)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Fair Algorithms for Clustering](https://mlsanity.com/paper/1901.02393/) (2019)
- [Scalable Fair Clustering](https://mlsanity.com/paper/1902.03519/) (2019)
- [Guarantees for Spectral Clustering with Fairness Constraints](https://mlsanity.com/paper/1901.08668/) (2019)
- [Proportionally Fair Clustering](https://mlsanity.com/paper/1905.03674/) (2019)
- [A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity](https://mlsanity.com/paper/1809.03400/) (2018)
- [Clustering without Over-Representation](https://mlsanity.com/paper/1905.12753/) (2019)
- [Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making](https://mlsanity.com/paper/1806.04959/) (2018)
- [On the cost of essentially fair clusterings](https://mlsanity.com/paper/1811.10319/) (2018)
- [Fair clustering via equitable group representations](https://mlsanity.com/paper/2006.11009/) (2020)
- [Fair Classification and Social Welfare](https://mlsanity.com/paper/1905.00147/) (2019)
- [Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach](https://mlsanity.com/paper/2008.00104/) (2020)
- [Probabilistic Fair Clustering](https://mlsanity.com/paper/2006.10916/) (2020)
- [A Center in Your Neighborhood: Fairness in Facility Location](https://mlsanity.com/paper/1908.09041/) (2019)
- [The Hardness of Approximation of Euclidean k-means](https://mlsanity.com/paper/1502.03316/) (2015)
- [Approximation Algorithms for Socially Fair Clustering](https://mlsanity.com/paper/2103.02512/) (2021)
- [Fair Clustering Under a Bounded Cost](https://mlsanity.com/paper/2106.07239/) (2021)
- [An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning](https://mlsanity.com/paper/2104.14504/) (2021)
- [Learning to be Fair: A Consequentialist Approach to Equitable Decision-Making](https://mlsanity.com/paper/2109.08792/) (2021)
- [Optimal Kidney Exchange with Immunosuppressants](https://mlsanity.com/paper/2103.02253/) (2021)
- [Doubly Constrained Fair Clustering](https://mlsanity.com/paper/2305.19475/) (2023)
- [On Uniform Capacitated k-Median Beyond the Natural LP Relaxation](https://mlsanity.com/paper/1409.6739/) (2014)
- [Hiring Under Uncertainty](https://mlsanity.com/paper/1905.02709/) (2019)
- [Scalable Robust Kidney Exchange](https://mlsanity.com/paper/1811.03532/) (2018)
- [Fairness through Social Welfare Optimization](https://mlsanity.com/paper/2102.00311/) (2021)
- [Fair Polylog-Approximate Low-Cost Hierarchical Clustering](https://mlsanity.com/paper/2311.12501/) (2023)
- [Fair Clustering: Critique, Caveats, and Future Directions](https://mlsanity.com/paper/2406.15960/) (2024)
- [The Fairness-Quality Trade-off in Clustering](https://mlsanity.com/paper/2408.10002/) (2024)

## Abstract (arXiv)

Fair clustering has traditionally focused on ensuring equitable group representation or equalizing group-specific clustering costs. However, Dickerson et al. (2025) recently showed that these fairness notions may yield undesirable or unintuitive clustering outcomes and advocated for a welfare-centric clustering approach that models the utilities of the groups. In this work, we model group utilities based on both distances and proportional representation and formalize two optimization objectives based on welfare-centric clustering: the Rawlsian (Egalitarian) objective and the Utilitarian objective. We introduce novel algorithms for both objectives and prove theoretical guarantees for them. Empirical evaluations on multiple real-world datasets demonstrate that our methods significantly outperform existing fair clustering baselines.