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

The Lancet Digital Health, pp. 101035

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.

Authors 7

1. Siri Lise van der Meijden corresponding Aachen

   Leiden University Medical Center

   Affiliation as printed

   Department of Intensive Care Medicine, Leiden University Medical Center, Leiden, Netherlands; Healthplus.ai, Amsterdam, Netherlands. Electronic address: s.l.van_der_meijden@lumc.nl

2. Yuqing Wang

   Stanford Medicine · Stanford University

   Affiliation as printed

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

3. Madelena Y. Ng

   Stanford Medicine · Stanford University

   Affiliation as printed

   Department of Medicine (Biomedical Informatics), Stanford University, Stanford, CA, USA; Laurie J. Girand Center for Biomedical Ethics at Stanford, Stanford University, Stanford, CA, USA

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

   Leiden University Medical Center

   Affiliation as printed

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

5. Bart F. Geerts
   Affiliation as printed

   Healthplus.ai, Amsterdam, Netherlands

6. Ewout Willem Steyerberg Aachen

   Leiden University Medical Center · University Medical Center Utrecht

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands; Julius Centre for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands

7. Tina Hernandez‐Boussard

   Stanford University

   Affiliation as printed

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