About | HeinOnline Law Journal Library | HeinOnline Law Journal Library | HeinOnline
LOG IN
LOG IN

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: WHEN AIs OUTPERFORM DOCTORS: CONFRONTING THE CHALLENGES OF A TORT- INDUCED OVER-RELIANCE ON MACHINE LEARNING 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.

What Is HeinOnline?

HeinOnline is a subscription-based resource containing thousands of academic and legal journals from inception; complete coverage of government documents such as U.S. Statutes at Large, U.S. Code, Federal Register, Code of Federal Regulations, U.S. Reports, and much more. Documents are image-based, fully searchable PDFs with the authority of print combined with the accessibility of a user-friendly and powerful database. For more information, request a quote or trial for your organization below.



Short-term subscription options include 24 hours, 48 hours, or 1 week to HeinOnline.

Purchase Access

Contact us for annual subscription options:

Contact Us

Already a HeinOnline Subscriber?

Log In

profiles profiles most