Paper accepted in Journal of Biomedical Informatics!

Fairness in machine learning has been extensively studied in supervised settings, where labeled outcomes allow direct assessment of bias. In contrast, fairness in unsupervised learning—particularly in healthcare remains insufficiently examined. In the absence of labels, it is unclear how fairness should be defined or evaluated for discovered structures such as patient subgroups, disease subtypes, or trajectories, despite their growing influence on clinical understanding and decision-making. This review aims to systematically examine how fairness is conceptualized, operationalized, and evaluated in unsupervised healthcare AI.