What’s in the Box?: Uncertain Accountability of Machine Learning Applications in Healthcare

The American Journal of Bioethics

Posted: 16 Dec 2020 Last revised: 8 Apr 2021

See all articles by Ma'n H. Zawati

Ma'n H. Zawati

McGill University - Centre of Genomics and Policy

Michael Lang

McGill University - Faculty of Law

Date Written: October 26, 2020

Abstract

Machine learning is an increasingly significant part of modern healthcare, transforming the way clinical decisions are made and health resources are managed (Wiens and Shenoy 2018). These developments have been closely scrutinized by bioethicists and legal scholars, who have identified machine learning’s potentially harmful impacts on patients and clinicians. Danton S. Char and colleagues have proposed a well defended pipeline model for identifying and addressing ethics concerns, with the goal of mitigating the harmful impacts of machine learning systems and helping to better integrate them into healthcare systems. The paper is an important and productive step toward ensuring that artificially intelligent tools can be used to safely promote human health. As the authors state explicitly, the proposed pipeline model does not address the issue of “who should be responsible for what,” but is rather intended to provide a structured framework in which to consider ethical implications raised by machine learning applications in health.

Suggested Citation

Zawati, Ma'n H. and Lang, Michael, What’s in the Box?: Uncertain Accountability of Machine Learning Applications in Healthcare (October 26, 2020). The American Journal of Bioethics, Available at SSRN: https://ssrn.com/abstract=3723772

Ma'n H. Zawati (Contact Author)

McGill University - Centre of Genomics and Policy ( email )

740 Dr. Penfield Avenue, Suite 5200
Montreal, Quebec H3A 0G1
Canada

Michael Lang

McGill University - Faculty of Law ( email )

3644 Peel Street
Montreal H3A 1W9, Quebec
Canada

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