Alvin Rajkomar, MD *; Michaela Hardt, PhD *; Michael D. Howell, MD, MPH; Greg Corrado, PhD; Marshall H. Chin, MD, MPH
Acknowledgment: The authors thank Meredith Whittaker, Roxanne Pinto, Moritz Hardt, Gerardo Flores, Charina Chou, Kathryn Rough, Ashley Hayes, Jutta Williams, Katherine Chou, Andrew Smart, Alex Beutel, Valeria Espinosa, Adam Sadilek, Kaspar Molzberger, and Yoni Halpern for insightful comments on the interplay of fairness in building and deploying machine learning and for helpful comments on early versions of the manuscript. They also thank John Fahrenbach, James Williams, and their colleagues from the Chicago Center for Diabetes Translation Research for providing critical analysis of the ideas of the manuscript applied to real clinical prediction tasks.
Grant Support: Dr. Chin was supported in part by the Chicago Center for Diabetes Translation Research (grant NIDDK P30 DK092949), Robert Wood Johnson Foundation Finding Answers: Solving Disparities Through Payment and Delivery Reform Program Office, and Merck Foundation Bridging the Gap: Reducing Disparities in Diabetes Care National Program Office.
Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-1990.
Corresponding Author: Alvin Rajkomar, MD, Google LLC, 1600 Amphitheatre Way, Mountain View, CA 94043; e-mail, email@example.com.
Current Author Addresses: Drs. Rajkomar, Hardt, Howell, and Corrado: Google LLC, 1600 Amphitheatre Way, Mountain View, CA 94043.
Dr. Chin: University of Chicago, 5841 South Maryland Avenue, MC2007, Chicago, IL 60637.
Author Contributions: Conception and design: A. Rajkomar, M. Hardt.
Analysis and interpretation of the data: A. Rajkomar, M. Hardt.
Drafting of the article: A. Rajkomar, M. Hardt, M.D. Howell.
Critical revision for important intellectual content: A. Rajkomar, M. Hardt, M.D. Howell, G. Corrado, M.H. Chin.
Final approval of the article: A. Rajkomar, M. Hardt, M.D. Howell, G. Corrado, M.H. Chin.
Statistical expertise: A. Rajkomar, M. Hardt.
Administrative, technical, or logistic support: M.D. Howell.
Machine learning is used increasingly in clinical care to improve diagnosis, treatment selection, and health system efficiency. Because machine-learning models learn from historically collected data, populations that have experienced human and structural biases in the past—called protected groups—are vulnerable to harm by incorrect predictions or withholding of resources. This article describes how model design, biases in data, and the interactions of model predictions with clinicians and patients may exacerbate health care disparities. Rather than simply guarding against these harms passively, machine-learning systems should be used proactively to advance health equity. For that goal to be achieved, principles of distributive justice must be incorporated into model design, deployment, and evaluation. The article describes several technical implementations of distributive justice—specifically those that ensure equality in patient outcomes, performance, and resource allocation—and guides clinicians as to when they should prioritize each principle. Machine learning is providing increasingly sophisticated decision support and population-level monitoring, and it should encode principles of justice to ensure that models benefit all patients.
Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring Fairness in Machine Learning to Advance Health Equity. Ann Intern Med. [Epub ahead of print ]:. doi: 10.7326/M18-1990
Download citation file:
Published: Ann Intern Med. 2018.
Healthcare Delivery and Policy.
Results provided by:
Copyright © 2018 American College of Physicians. All Rights Reserved.
Print ISSN: 0003-4819 | Online ISSN: 1539-3704
Conditions of Use