Navigating Fairness in AI-based Prediction Models: Theoretical Constructs and Practical Applications.

ABSTRACT: Artificial Intelligence (AI)-based prediction models, including risk scoring systems and decision support systems, are increasingly adopted in healthcare. Addressing AI fairness is essential to fighting health disparities and achieving equitable performance and patient outcomes. Numerous and conflicting definitions of fairness complicate this effort. This paper aims to structure the transition of AI fairness from theory to practical application with appropriate fairness metrics. For 27 definitions of fairness identified in the recent literature, we assess the relation with the model's intended use, type of decision influenced and ethical principles of distributive justice. We advocate that due to limitations in some notions of fairness, clinical utility, performance-based metrics (area und

SUBMITTER: van der Meijden SL 

PROVIDER: S-EPMC11974802 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature