What Is Fair? Defining Fairness in Machine Learning for Health.

Jianhui Gao¹, Benson Chou¹, Zachary R McCaw², Hilary Thurston³, Paul Varghese⁴, Chuan Hong⁵, Jessica Gronsbell^1,6,7

¹Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.

²Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA.

³Department of Gender, Feminist & Women's Studies, York University, Toronto, Ontario, Canada.

⁴Verily Life Sciences, Cambridge, Massachusetts, USA.

⁵Department of Biostatistics, Duke University Raleigh, North Carolina, USA.

⁶Department of Family & Community Medicine, University of Toronto, Toronto, Ontario, Canada.

⁷Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.

PubMed PMC DOI PDF

Abstract

Ensuring that machine-learning (ML) models are safe, effective, and equitable across all patients is critical for clinical decision-making and for preventing the amplification of existing health disparities. In this work, we examine how fairness is conceptualized in ML for health, including why ML models may lead to unfair decisions and how fairness has been measured in diverse real-world applicat…

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