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Commentary
Equity in essence: a call for operationalising fairness in machine learning for healthcare

Authors

  • Judy Wawira Gichoya Department of Radiology & Imaging Sciences, Emory University, Atlanta, Georgia, USAFogarty International Center, National Institutes of Health (NIH), Bethesda, Maryland, USA PubMed articlesGoogle scholar articles
  • Liam G McCoy Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada PubMed articlesGoogle scholar articles
  • Leo Anthony Celi Laboratory for Computational Physiology, Harvard-MIT Division of Health Sciences and Technology, Cambridge, Massachusetts, USADivision of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USADepartment of Biostatistics, Harvrd T.H. Chan School of Public Health, Boston, Massachusetts, USA PubMed articlesGoogle scholar articles
  • Marzyeh Ghassemi Department of Computer Science, University of Toronto, Toronto, Ontario, CanadaDepartment of Medicine, University of Toronto, Toronto, Ontario, CanadaVector Institute for Artificial Intelligence, Toronto, Ontario, Canada PubMed articlesGoogle scholar articles
  1. Correspondence to Liam G McCoy; liam.mccoy{at}mail.utoronto.ca

Citation

Wawira Gichoya J, McCoy LG, Celi LA, et al
Equity in essence: a call for operationalising fairness in machine learning for healthcare
BMJ Health & Care Informatics 2021;28:e100289. doi: 10.1136/bmjhci-2020-100289

Publication history

  • Received November 22, 2020
  • Revision received February 7, 2021
  • Accepted February 9, 2021
  • First published April 28, 2021.
Online issue publication 
July 06, 2022

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Copyright information

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.