Machine Learning, Health Disparities, and Causal Reasoning

Abstract

Editorials18 December 2018Machine Learning, Health Disparities, and Causal ReasoningSteven N. Goodman, MD, MHS, PhD, Sharad Goel, MS, PhD, and Mark R. Cullen, MDSteven N. Goodman, MD, MHS, PhDStanford University School of Medicine, Stanford, California (S.N.G., M.R.C.), Sharad Goel, MS, PhDStanford University School of Engineering, Stanford, California (S.G.), and Mark R. Cullen, MDStanford University School of Medicine, Stanford, California (S.N.G., M.R.C.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M18-3297 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail In their current Annals article, Rajkomar and colleagues (1) warn us that the introduction of machine-learned predictive algorithms into medicine might inadvertently reinforce or create inequitable treatment of protected groups, for which the computer science community has adopted the terminology of "fairness." Others have noted the need for careful, ethical scrutiny of these models (2), and Rajkomar and colleagues add to those calls an elaborate taxonomy of pitfalls and an oversight structure to minimize the ethical harms.The authors offer some useful constructs, many of which have direct counterparts in clinical research, epidemiology, and implementation science (such as "training–serving skew" ...References1. Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169:866-72. doi:10.7326/M18-1990 LinkGoogle Scholar2. Char DS, Shah NH, Magnus D. Implementing machine learning in health care - addressing ethical challenges. N Engl J Med. 2018;378:981-983. [PMID: 29539284] CrossrefMedlineGoogle Scholar3. Corbett-Davies S, Goel S. The measure and mismeasure of fairness: a critical review of fair machine learning. Accessed at https://arxiv.org/abs/1808.00023 on 16 November 2018. Google Scholar4. Buolamwini J, Gebru T. Gender shades: intersectional accuracy disparities in commercial gender classification. Proceedings of Machine Learning Research: Conference on Fairness, Accountability, and Transparency. 2018;81:1-15. Accessed at http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf on 26 November 2018. Google Scholar5. Pearl J, Mackenzie D. The Book of Why: The New Science of Cause and Effect. New York: Basic Books; 2018. Google Scholar Author, Article, and Disclosure InformationAffiliations: Stanford University School of Medicine, Stanford, California (S.N.G., M.R.C.)Stanford University School of Engineering, Stanford, California (S.G.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-3297.Corresponding Author: Steven N. Goodman, MD, MHS, PhD, Stanford University School of Medicine, 150 Governor's Lane, HRP/Redwood Building T265, Stanford, CA 94305; e-mail, Steve.[email protected]edu.Current Author Addresses: Dr. Goodman: Stanford University School of Medicine, 150 Governor's Lane, HRP/Redwood Building T265, Stanford, CA 94305.Dr. Goel: Stanford School of Engineering, 475 Via Ortega, Stanford, CA 94305.Dr. Cullen: Stanford Center for Population Health Sciences, 1070 Arastradero Road, Palo Alto, CA 94304.This article was published at Annals.org on 4 December 2018. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoEnsuring Fairness in Machine Learning to Advance Health Equity Alvin Rajkomar , Michaela Hardt , Michael D. Howell , Greg Corrado , and Marshall H. 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