Fair Clustering: A Causal Perspective
This addresses fairness in unsupervised learning for domains like decision-making, offering a more nuanced method but is incremental as it builds on existing fair clustering with causal insights.
The paper tackled the problem of clustering algorithms propagating disparities by showing that non-causal fairness notions can induce discrimination, and presented a causal fairness approach that effectively minimizes specified metrics on biased datasets.
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.