Fair Hierarchical Clustering

Understand

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.