What is Fair? Defining Fairness in Machine Learning for Health

• URL: http://arxiv.org/abs/2406.09307v4
• Date: Thu, 14 Nov 2024 18:45:35 GMT
• Title: What is Fair? Defining Fairness in Machine Learning for Health
• Authors: Jianhui Gao, Benson Chou, Zachary R. McCaw, Hilary Thurston, Paul Varghese, Chuan Hong, Jessica Gronsbell,
• Abstract summary: This review examines notions of fairness used in machine learning for health. We provide an overview of commonly used fairness metrics and supplement our discussion with a case-study of an openly available electronic health record dataset. We also discuss the outlook for future research, highlighting current challenges and opportunities in defining fairness in health.
• Score: 0.6311610943467981
• License:
• Abstract: Ensuring that machine learning (ML) models are safe, effective, and equitable across all patient groups is essential for clinical decision-making and for preventing the reinforcement of existing health disparities. This review examines notions of fairness used in ML for health, including a review of why ML models can be unfair and how fairness has been quantified in a wide range of real-world examples. We provide an overview of commonly used fairness metrics and supplement our discussion with a case-study of an openly available electronic health record (EHR) dataset. We also discuss the outlook for future research, highlighting current challenges and opportunities in defining fairness in health.

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