  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.
