Fairness Beyond Demographics: Optimizing Performance Across Appearance-Based Hidden Cohorts in Medical Imaging

ArXi:2605.29827v1 Announce Type: new Medical image analysis models can exhibit performance disparities across patient subgroups, threatening clinical safety and fairness. Existing methods typically address this issue by optimizing accuracy and fairness metrics for visible graphic attributes (e.g., sex or age) considered in isolation. This strategy not only overlooks potentially informative latent stratifications, which may reveal deeper sources of model error and inequity, but also fails to scale when multiple graphic attributes are considered simultaneously due to the resulting sparsity of.