MAPPE: Rethinking and Improving Fairness in LLMs for Medicine via Minimax Preference-based Prompt Evolution
This paper introduces universal fairness, a clinically grounded definition that reframes fairness as maximizing subgroup-aware diagnostic performance under attribute-conditioned health disparities, and proposes MAPPE, a training-free minimax prompt optimization framework that theoretically promotes universal fairness.