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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Bibliographische Detailangaben
Veröffentlicht in:	Nature medicine Jg. 26; H. 1; S. 25 - 26
Hauptverfasser:	Wiens, Jenna, Price, W. Nicholson, Sjoding, Michael W.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Nature Publishing Group US 01.01.2020 Nature Publishing Group
Schlagworte:	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-Zugang:	Volltext
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