mlsanityData to 7 Oct 2026

Fair Hierarchical Clustering

arXiv 2006.10221 ↗First posted 18 Jun 2020cs.DS cs.LG stat.MLcited by 25

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Abstract (arXiv)

As machine learning has become more prevalent, researchers have begun to recognize the necessity of ensuring machine learning systems are fair. Recently, there has been an interest in defining a notion of fairness that mitigates over-representation in traditional clustering. In this paper we extend this notion to hierarchical clustering, where the goal is to recursively partition the data to optimize a specific objective. For various natural objectives, we obtain simple, efficient algorithms to find a provably good fair hierarchical clustering. Empirically, we show that our algorithms can find a fair hierarchical clustering, with only a negligible loss in the objective.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 3; 2023: 8; 2024: 4; 2025: 3; 2026: 1.

25 papers.

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  1. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  2. 2021Fair Clustering Under a Bounded Costcited by 21
  3. 2023Doubly Constrained Fair Clusteringcited by 9
  4. 2022Individual Preference Stability for Clusteringcited by 8
  5. 2024A Scalable Algorithm for Individually Fair K-means Clusteringcited by 8
  6. 2022Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Costcited by 4
  7. 2023Holistic Survey of Privacy and Fairness in Machine Learningcited by 4
  8. 2021Modification-Fair Cluster Editingcited by 3
  9. 2023A review of clustering models in educational data science towards fairness-aware learningcited by 3
  10. 2023Hierarchical clustering with dot products recovers hidden tree structurecited by 2
  11. 2023Fair Polylog-Approximate Low-Cost Hierarchical Clusteringcited by 2
  12. 2024On Socially Fair Low-Rank Approximation and Column Subset Selectioncited by 2
13 more
  1. 2021Hierarchical Clustering: New Bounds and Objectivecited by 1
  2. 2023Fair Clustering via Hierarchical Fair-Dirichlet Processcited by 1
  3. 2023Dual Node and Edge Fairness-Aware Graph Partitioncited by 1
  4. 2024A Note on Bias to Completecited by 1
  5. 2025Adversarial Fair Multi-View Clusteringcited by 1
  6. 2021Coping with Mistreatment in Fair Algorithmscited by 0
  7. 2021CONFAIR: Configurable and Interpretable Algorithmic Fairnesscited by 0
  8. 2022Cluster-level Group Representativity Fairness in k-means Clusteringcited by 0
  9. 2023Fair Minimum Representation Clusteringcited by 0
  10. 2024Fair Clustering with Minimum Representation Constraintscited by 0
  11. 2025Interpretable Fair Clusteringcited by 0
  12. 2025Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clusteringcited by 0
  13. 2026Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancycited by 0