Navigating fairness in artificial intelligence-based prediction models: theoretical constructs and practical applications.

Abstract

Artificial intelligence (AI)-based prediction models, including risk scoring systems and decision support systems, are being increasingly adopted in health care. Addressing AI fairness is essential to fighting health disparities and ensuring equitable model performance and patient outcomes. However, numerous and conflicting definitions of fairness complicate this effort. In this Viewpoint, we aim to support the transition of AI fairness from theory to practice using appropriate fairness metrics. We assess the relation of 27 fairness definitions identified in the literature to the model's intended use, type of decision influenced, and ethical principles of distributive justice. Because of limitations in some notions of fairness, we argue that clinical utility, performance-based metrics (such as area under the receiver operating characteristic curve), calibration, and statistical parity are the most relevant group-based metrics for medical applications. Through two use cases, we show that different metrics might be applicable depending on the intended use and ethical framework. Our approach provides practical guidance for fair AI development, helping AI developers and assessors to evaluate model fairness and understand the effects of bias mitigation strategies, thereby supporting equitable AI-based implementations.