  The advent of ML-driven decision-making and policy formation has led to an
increasing focus on algorithmic fairness. As clustering is one of the most
commonly used unsupervised machine learning approaches, there has naturally
been a proliferation of literature on {\em fair clustering}. A popular notion
of fairness in clustering mandates the clusters to be {\em balanced}, i.e.,
each level of a protected attribute must be approximately equally represented
in each cluster. Building upon the original framework, this literature has
rapidly expanded in various aspects. In this article, we offer a novel
model-based formulation of fair clustering, complementing the existing
literature which is almost exclusively based on optimizing appropriate
objective functions.
