Mitigating Racial Bias in Machine Learning

Kristin M. Kostick-Quenet; I. Glenn Cohen; Sara Gerke; Bernard Lo; James Antaki; Faezah Movahedi; Hasna Njah; Lauren Schoen; Jerry E. Estep; J. S. Blumenthal-Barby

doi:10.1017/jme.2022.13

Mitigating Racial Bias in Machine Learning

Kristin M. Kostick-Quenet, I. Glenn Cohen, Sara Gerke, Bernard Lo, James Antaki, Faezah Movahedi, Hasna Njah, Lauren Schoen, Jerry E. Estep, J. S. Blumenthal-Barby

Research output: Contribution to journal › Article › peer-review

10 Citations (SciVal)

Abstract

When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvantaged groups.

Original language	English (US)
Pages (from-to)	92-100
Number of pages	9
Journal	Law, medicine & health care : a publication of the American Society of Law & Medicine
Volume	50
Issue number	1
DOIs	https://doi.org/10.1017/jme.2022.13
State	Published - 2022

All Science Journal Classification (ASJC) codes

Issues, ethics and legal aspects
Health Policy

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10.1017/jme.2022.13

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title = "Mitigating Racial Bias in Machine Learning",

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Mitigating Racial Bias in Machine Learning

Abstract

All Science Journal Classification (ASJC) codes

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