Fair Hierarchical Clustering
This work addresses fairness in machine learning for hierarchical clustering, offering incremental improvements by adapting existing fairness concepts to a new context.
The paper tackles the problem of ensuring fairness in hierarchical clustering by extending fairness notions from traditional clustering to hierarchical settings, and it provides simple, efficient algorithms that achieve provably good fair hierarchical clustering with only negligible loss in objective performance.
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.