Fair Hierarchical Clustering
Data Structures and Algorithms
2020-06-22 v2 Machine Learning
Machine Learning
Abstract
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.
Cite
@article{arxiv.2006.10221,
title = {Fair Hierarchical Clustering},
author = {Sara Ahmadian and Alessandro Epasto and Marina Knittel and Ravi Kumar and Mohammad Mahdian and Benjamin Moseley and Philip Pham and Sergei Vassilvitskii and Yuyan Wang},
journal= {arXiv preprint arXiv:2006.10221},
year = {2020}
}