mlsanityData to 7 Oct 2026

Robust Fair Clustering with Group Membership Uncertainty Sets

arXiv 2406.00599 ↗First posted 2 Jun 2024cs.LG cs.AI cs.CYcited by 1

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Abstract (arXiv)

We study the canonical fair clustering problem where each cluster is constrained to have close to population-level representation of each group. Despite significant attention, the salient issue of having incomplete knowledge about the group membership of each point has been superficially addressed. In this paper, we consider a setting where the assigned group memberships are noisy. We introduce a simple noise model that requires a small number of parameters to be given by the decision maker. We then present an algorithm for fair clustering with provable robustness guarantees. Our framework enables the decision maker to trade off between the robustness and the clustering quality. Unlike previous work, our algorithms are backed by worst-case theoretical guarantees. Finally, we empirically verify the performance of our algorithm on real world datasets and show its superior performance over existing baselines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 42 references that are in the map. First posted on arXiv 2 Jun 2024.

Builds on

25 of 42 references.

References · 25

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2014Certifying and removing disparate impactcited by 715
  3. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  4. 2018Fair Clustering Through Fairletscited by 176
  5. 2019Fair Algorithms for Clusteringcited by 98
  6. 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
  7. 2019Scalable Fair Clusteringcited by 78
  8. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  9. 2019Guarantees for Spectral Clustering with Fairness Constraintscited by 72
  10. 2019Clustering without Over-Representationcited by 50
  11. 2019Equalized odds postprocessing under imperfect group informationcited by 48
  12. 2018On the cost of essentially fair clusteringscited by 44
13 more
  1. 2019Noise-tolerant fair classificationcited by 40
  2. 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
  3. 2020Mitigating Bias in Set Selection with Noisy Protected Attributescited by 29
  4. 2020Probabilistic Fair Clusteringcited by 28
  5. 2020Fair Correlation Clusteringcited by 27
  6. 2021Fair Clustering Under a Bounded Costcited by 21
  7. 2022Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metriccited by 17
  8. 2022Fair Ranking with Noisy Protected Attributescited by 17
  9. 2022Scalable Spectral Clustering with Group Fairness Constraintscited by 16
  10. 2022Robust Fair Clustering: A Novel Fairness Attack and Defense Frameworkcited by 13
  11. 2023Doubly Constrained Fair Clusteringcited by 9
  12. 2022Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Costcited by 4
  13. 2023Fair Polylog-Approximate Low-Cost Hierarchical Clusteringcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Sub-4 Approximation for Fair k-Meanscited by 0