mlsanityData to 14 Oct 2026

Welfare-Centric Clustering

arXiv 2508.10345 ↗First posted 14 Aug 2025cs.LG cs.AI cs.CYcited by 0

Not on the map. Why?

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.

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

30 of 45 references.

References · 30

  1. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  2. 2018Fair Clustering Through Fairletscited by 176
  3. 2019Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practicescited by 174
  4. 2019Fair Algorithms for Clusteringcited by 98
  5. 2019Scalable Fair Clusteringcited by 78
  6. 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
  7. 2019Proportionally Fair Clusteringcited by 69
  8. 2018A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunitycited by 53
  9. 2019Clustering without Over-Representationcited by 50
  10. 2018Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Makingcited by 46
  11. 2018On the cost of essentially fair clusteringscited by 44
  12. 2020Fair clustering via equitable group representationscited by 43
18 more
  1. 2019Fair Classification and Social Welfarecited by 40
  2. 2020Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approachcited by 32
  3. 2020Probabilistic Fair Clusteringcited by 28
  4. 2019A Center in Your Neighborhood: Fairness in Facility Locationcited by 26
  5. 2015The Hardness of Approximation of Euclidean k-meanscited by 25
  6. 2021Approximation Algorithms for Socially Fair Clusteringcited by 25
  7. 2021Fair Clustering Under a Bounded Costcited by 21
  8. 2021An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learningcited by 18
  9. 2021Learning to be Fair: A Consequentialist Approach to Equitable Decision-Makingcited by 11
  10. 2021Optimal Kidney Exchange with Immunosuppressantscited by 9
  11. 2023Doubly Constrained Fair Clusteringcited by 9
  12. 2014On Uniform Capacitated k-Median Beyond the Natural LP Relaxationcited by 4
  13. 2019Hiring Under Uncertaintycited by 4
  14. 2018Scalable Robust Kidney Exchangecited by 3
  15. 2021Fairness through Social Welfare Optimizationcited by 3
  16. 2023Fair Polylog-Approximate Low-Cost Hierarchical Clusteringcited by 2
  17. 2024Fair Clustering: Critique, Caveats, and Future Directionscited by 1
  18. 2024The Fairness-Quality Trade-off in Clusteringcited by 1

Built on by

Not cited yet.