# Fair Algorithms for Hierarchical Agglomerative Clustering

arXiv: https://arxiv.org/abs/2005.03197 · HTML: https://mlsanity.com/paper/2005.03197/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 10 of 10)

- [Robust Fair Clustering: A Novel Fairness Attack and Defense Framework](https://mlsanity.com/paper/2210.01953/) (2022)
- [Fair-Capacitated Clustering](https://mlsanity.com/paper/2104.12116/) (2021)
- [Doubly Constrained Fair Clustering](https://mlsanity.com/paper/2305.19475/) (2023)
- [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)
- [Hierarchical clustering with dot products recovers hidden tree structure](https://mlsanity.com/paper/2305.15022/) (2023)
- [Coping with Mistreatment in Fair Algorithms](https://mlsanity.com/paper/2102.10750/) (2021)
- [Fair Minimum Representation Clustering](https://mlsanity.com/paper/2302.03151/) (2023)
- [Fair Clustering with Minimum Representation Constraints](https://mlsanity.com/paper/2409.02963/) (2024)
- [Incorporating Fairness in Neighborhood Graphs for Fair Spectral Clustering](https://mlsanity.com/paper/2512.09810/) (2025)

## Abstract (arXiv)

Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data science, and seek to partition the dataset into clusters while generating a hierarchical relationship between the data samples. HAC algorithms are employed in many applications, such as biology, natural language processing, and recommender systems. Thus, it is imperative to ensure that these algorithms are fair -- even if the dataset contains biases against certain protected groups, the cluster outputs generated should not discriminate against samples from any of these groups. However, recent work in clustering fairness has mostly focused on center-based clustering algorithms, such as k-median and k-means clustering. In this paper, we propose fair algorithms for performing HAC that enforce fairness constraints 1) irrespective of the distance linkage criteria used, 2) generalize to any natural measures of clustering fairness for HAC, 3) work for multiple protected groups, and 4) have competitive running times to vanilla HAC. Through extensive experiments on multiple real-world UCI datasets, we show that our proposed algorithm finds fairer clusterings compared to vanilla HAC as well as other state-of-the-art fair clustering approaches.