# Fair Hierarchical Clustering

arXiv: https://arxiv.org/abs/2006.10221 · HTML: https://mlsanity.com/paper/2006.10221/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2020.

## Built on by (most cited first, 25 of 25)

- [A Sociotechnical View of Algorithmic Fairness](https://mlsanity.com/paper/2110.09253/) (2021)
- [Fair Clustering Under a Bounded Cost](https://mlsanity.com/paper/2106.07239/) (2021)
- [Doubly Constrained Fair Clustering](https://mlsanity.com/paper/2305.19475/) (2023)
- [Individual Preference Stability for Clustering](https://mlsanity.com/paper/2207.03600/) (2022)
- [A Scalable Algorithm for Individually Fair K-means Clustering](https://mlsanity.com/paper/2402.06730/) (2024)
- [Generalized Reductions: Making any Hierarchical Clustering Fair and Balanced with Low Cost](https://mlsanity.com/paper/2205.14198/) (2022)
- [Holistic Survey of Privacy and Fairness in Machine Learning](https://mlsanity.com/paper/2307.15838/) (2023)
- [Modification-Fair Cluster Editing](https://mlsanity.com/paper/2112.03183/) (2021)
- [A review of clustering models in educational data science towards fairness-aware learning](https://mlsanity.com/paper/2301.03421/) (2023)
- [Hierarchical clustering with dot products recovers hidden tree structure](https://mlsanity.com/paper/2305.15022/) (2023)
- [Fair Polylog-Approximate Low-Cost Hierarchical Clustering](https://mlsanity.com/paper/2311.12501/) (2023)
- [On Socially Fair Low-Rank Approximation and Column Subset Selection](https://mlsanity.com/paper/2412.06063/) (2024)
- [Hierarchical Clustering: New Bounds and Objective](https://mlsanity.com/paper/2111.06863/) (2021)
- [Fair Clustering via Hierarchical Fair-Dirichlet Process](https://mlsanity.com/paper/2305.17557/) (2023)
- [Dual Node and Edge Fairness-Aware Graph Partition](https://mlsanity.com/paper/2306.10123/) (2023)
- [A Note on Bias to Complete](https://mlsanity.com/paper/2402.11710/) (2024)
- [Adversarial Fair Multi-View Clustering](https://mlsanity.com/paper/2508.04071/) (2025)
- [Coping with Mistreatment in Fair Algorithms](https://mlsanity.com/paper/2102.10750/) (2021)
- [CONFAIR: Configurable and Interpretable Algorithmic Fairness](https://mlsanity.com/paper/2111.08878/) (2021)
- [Cluster-level Group Representativity Fairness in k-means Clustering](https://mlsanity.com/paper/2212.14467/) (2022)
- [Fair Minimum Representation Clustering](https://mlsanity.com/paper/2302.03151/) (2023)
- [Fair Clustering with Minimum Representation Constraints](https://mlsanity.com/paper/2409.02963/) (2024)
- [Interpretable Fair Clustering](https://mlsanity.com/paper/2511.21109/) (2025)
- [Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clustering](https://mlsanity.com/paper/2512.09810/) (2025)
- [Multi-View Fair Clustering Guided by Cross-View Sensitive Information Discrepancy](https://mlsanity.com/paper/2609.25811/) (2026)

## 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.