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61 Ariz. L. Rev. 33 (2019)
When AIs Outperform Doctors: Confronting the Challenges of a Tort-Induced over-Reliance on Machine Learning

handle is hein.journals/arz61 and id is 40 raw text is: 





       A. Michael Froomkin,* Ian Kerr** & Joelle Pineau***

Someday, perhaps soon, diagnostics generated by machine learning (ML) will
have demonstrably better success rates than those generated by human doctors.
What will the dominance of ML diagnostics mean for medical malpractice law, for
the future of medical service provision, for the demand for certain kinds of
doctors, and in the long run for the quality of medical diagnostics itself?
This Article argues that once ML diagnosticians, such as those based on neural
networks, are shown to be superior, existing medical malpractice law will require
superior ML-generated medical diagnostics as the standard of care in clinical
settings. Further, unless implemented carefully, a physician's duty to use ML
systems in medical diagnostics could, paradoxically, undermine the very safety
standard that malpractice law set out to achieve. Although at first doctor +
machine may be more effective than either alone because humans and ML systems
might make very different kinds of mistakes, in time, as ML systems improve,

       *    Laurie Silvers & Mitchell Rubenstein Distinguished Professor of Law,
University of Miami. Member, University of Miami Center for Computational Science;
Fellow, Yale ISP. Thanks to Peter Asaro, Jack Balkin, Caroline Bradley, Ryan Calo, Kate
Crawford, Brad DeLong, Ed Felten, Colleen Flood, Jonathan Frankle, David Froomkin, Sue
Gluck, Bob Glushko, James Grimmelmann, Woody Hartzog, Margot Kaminski, Gregory
Keating, Daniel Kluttz, Mark Lemley, Amanda Levendowski, Christopher Millard, Deirdre
Mulligan, Helen Nissenbaum, Paul Ohm, Frank Pasquale, Laurel Riek, Cynthia Rudin,
Robin Schard, Andrew Selbst, Latanya Sweeney, participants in a University of Miami
School of Law faculty seminar, participants in a Yale ISP Ideas Lunch, and participants in
We Robot 2018 for advice and information. Thanks to the Social Sciences and Humanities
Research Council and the Canada Research Chairs program for their generous support.
      **    Canada Research Chair in Ethics, Law & Technology, University of Ottawa,
Faculty of Law, with cross appointments to the Faculty of Medicine, Department of
Philosophy, and School of Information Studies.
     ***    William Dawson Scholar and Associate Professor, School of Computer
Science, McGill University.

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