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

medRxiv

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 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 demonstrate that different metrics may be applicable depending on the intended use and ethical framework. Our approach provides a foundation for AI developers and assessors by assessing model fairness and the impact of bias mitigation strategies, hence promoting more equitable AI-based implementations.

Authors 6

1. Siri Lise van der Meijden corresponding Aachen Department of Intensive Care Medicine

   Leiden University Medical Center

   Affiliation as printed

   Department of Intensive Care Medicine, The Leiden University Medical Center, Leiden, The Netherlands

   Healthplus.ai B.V

   Leiden University Medical Center, Healthplus.ai

2. Yuqing Wang

   Stanford Medicine · Stanford University

   Affiliation as printed

   Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA

   Stanford University

3. M. Sesmu Arbous Aachen Department of Intensive Care Medicine

   Leiden University Medical Center

   Affiliation as printed

   Department of Intensive Care Medicine, The Leiden University Medical Center, Leiden, The Netherlands

   Leiden University Medical Center

4. Bart F. Geerts
   Affiliation as printed

   Healthplus.ai B.V

   Healthplus.ai

5. Ewout Willem Steyerberg Aachen Department of Biomedical Data Sciences

   Utrecht University · Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands

   Utrecht University Medical Center

6. Tina Hernandez‐Boussard

   Stanford Medicine · Stanford University

   Affiliation as printed

   Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

   Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA

   Stanford University