The Potential For Bias In Machine Learning And Opportunities For Health Insurers To Address It

Abstract

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.

Publication
Health Affairs
Irene Chen
Irene Chen
PhD Student

Irene works on machine learning methods for equitable healthcare. Her research focuses on two main areas, 1) developing machine learning methods for equitable clinical care, and 2) auditing and addressing algorithmic bias.

David Sontag
David Sontag
Professor of EECS

My research focuses on advancing machine learning and artificial intelligence, and using these to transform health care.

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