Diagnosing bias in data-driven algorithms for healthcare

A recent analysis highlighting the potential for algorithms to perpetuate existing racial biases in healthcare underscores the importance of thinking carefully about the labels used during algorithm development.

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Bibliographic Details
Published in:Nature medicine Vol. 26; no. 1; pp. 25 - 26
Main Authors: Wiens, Jenna, Price, W. Nicholson, Sjoding, Michael W.
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.01.2020
Nature Publishing Group
Subjects:
692/499
706/648
Algorithms
Bias
Biomedical and Life Sciences
Biomedicine
Cancer Research
Costs and Cost Analysis
Data Analysis
Delivery of Health Care
Electronic data processing
Health aspects
Health care
Health Expenditures
Humans
Infectious Diseases
Machine Learning
Medical care
Metabolic Diseases
Molecular Medicine
Neurosciences
News & Views
news-and-views
Prejudices
Social aspects
Technology application
United States
United States
ISSN:1078-8956, 1546-170X, 1546-170X
Online Access:Get full text
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Description
Summary:A recent analysis highlighting the potential for algorithms to perpetuate existing racial biases in healthcare underscores the importance of thinking carefully about the labels used during algorithm development.
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ISSN:1078-8956
1546-170X
1546-170X
DOI:10.1038/s41591-019-0726-6