  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.
