Machine learning, health disparities, and causal reasoning

Annals of Internal Medicine

Volumn 169, Issue 12, 2018, Pages 883-885

  Goodman, Steven N ^a   Goel, Sharad ^b   Cullen, Mark R ^a  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIETARY PATTERN; EDITORIAL; FOLLOW UP; HEALTH DISPARITY; HUMAN; MACHINE LEARNING; PRIORITY JOURNAL; PROGNOSIS; SURVIVAL; HEALTH EQUITY; PROBLEM SOLVING;

HEALTH EQUITY; MACHINE LEARNING; PROBLEM SOLVING;

EID: 85058735935     PISSN: 00034819     EISSN: 15393704     Source Type: Journal    
DOI: 10.7326/M18-3297     Document Type: Editorial

References (5)

1. 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
2. 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]
3. 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.
4. 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.
5. Pearl J, Mackenzie D. The Book of Why: The New Science of Cause and Effect. New York: Basic Books; 2018.