The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it.

Bibliographic Details
Main Authors:	Gervasi, Stephanie S (Author), Chen, Irene Y (Author), Smith-McLallen, Aaron (Author), Sontag, David (Author), Obermeyer, Ziad (Author), Vennera, Michael (Author), Chawla, Ravi (Author)
Format:	Article
Language:	English
Published:	Health Affairs (Project Hope), 2022-07-20T16:38:14Z.
Subjects:	Article
Online Access:	Get fulltext


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001	143894
042			\|a dc
100	1	0	\|a Gervasi, Stephanie S \|e author
700	1	0	\|a Chen, Irene Y \|e author
700	1	0	\|a Smith-McLallen, Aaron \|e author
700	1	0	\|a Sontag, David \|e author
700	1	0	\|a Obermeyer, Ziad \|e author
700	1	0	\|a Vennera, Michael \|e author
700	1	0	\|a Chawla, Ravi \|e author
245	0	0	\|a The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It: Article examines the potential for bias in machine learning and opportunities for health insurers to address it.
260			\|b Health Affairs (Project Hope), \|c 2022-07-20T16:38:14Z.
856			\|z Get fulltext \|u https://hdl.handle.net/1721.1/143894
546			\|a en
655	7		\|a Article
773			\|t 10.1377/HLTHAFF.2021.01287
773			\|t Health Affairs

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