  Clustering algorithms may unintentionally propagate or intensify existing
disparities, leading to unfair representations or biased decision-making.
Current fair clustering methods rely on notions of fairness that do not capture
any information on the underlying causal mechanisms. We show that optimising
for non-causal fairness notions can paradoxically induce direct discriminatory
effects from a causal standpoint. We present a clustering approach that
incorporates causal fairness metrics to provide a more nuanced approach to
fairness in unsupervised learning. Our approach enables the specification of
the causal fairness metrics that should be minimised. We demonstrate the
efficacy of our methodology using datasets known to harbour unfair biases.
