Open AccessThe Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It
Author(s) -
Stephanie S Gervasi,
Irene Y. Chen,
Aaron SmithMcLallen,
David Sontag,
Ziad Obermeyer,
Michael Vennera,
Ravi Chawla
Publication year - 2022
Publication title -
health affairs
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.837
H-Index - 178
eISSN - 2694-233X
pISSN - 0278-2715
DOI - 10.1377/hlthaff.2021.01287
Subject(s) - health care , analytics , equity (law) , data science , machine learning , computer science , health insurance , actuarial science , business , artificial intelligence , economics , political science , law , economic growth
As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed and used in clinical and business decisions. We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest mechanisms for identifying and dealing with bias and discuss challenges and opportunities to increase fairness through analytics in the health insurance industry.