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Ethical Machine Learning in Healthcare

  • Irene Y. Chen
  • , Emma Pierson
  • , Sherri Rose
  • , Shalmali Joshi
  • , Kadija Ferryman
  • , Marzyeh Ghassemi

Research output: Contribution to journal › Review article › peer-review

Abstract

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.

Original languageEnglish (US)
Pages (from-to)123-144
Number of pages22
JournalAnnual review of biomedical data science
Volume4
DOIs
StatePublished - May 6 2021
Externally publishedYes

Keywords

  • bias
  • ethics
  • health
  • health disparities
  • healthcare
  • machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Biochemistry, Genetics and Molecular Biology (miscellaneous)
  • Genetics
  • Cancer Research

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