Waymo Self-driving car
Source: Grendelkhan
Since autonomous car tend to have trouble with bad weather–snow and sometimes rain–flat, sunny Florida would seem to be a natural testing grounds. Indeed, Ford is supposedly running or planning to run a test in Miami, although I haven’t heard of actual sightings yet. Then again, we have some of the craziest drivers in the US, which could be seen as a positive or negative, depending on what sort of torture test you want to give the AIs.
I’m in Ottawa today for the Machine M.D. conference. My panel, on “Regulating Health and Safety” started at 8:30am, so now that it’s over I get to enjoy the rest of the very packed schedule.
(I’m missing the also wonderful annual Privacy Law Scholars conference to be here, an example of the difficulties in trying to write in, and keep up in, multiple areas even within technology law.)
My argument in my talk was that, contrary to a number of articles by others that are now in press, we don’t want a super-AI regulator, but rather need to find a way to strengthen the AI capacity of existing sectoral regulators like the FDA, NHTSA and many others. The exception(s) to this general rule arise(s) only if the AI aspect of a regulatory question predominates over the sectoral — something I argue is rare, and probably limited to issues regarding access to data, to data quality, to job losses due to AI, and possibly to the regulation (or at least liability for) AI emergent behavior.
Unsurprisingly, some members of the audience, many of whom are health professionals, pushed back against the idea that when it comes to AI health really isn’t all that different from transport, finance, sentencing, or other predictive profiling applications. A polite discussion — we are after all in Canada — ensued.
I’m proud to be part of the editorial board committee of the brand new Journal of Technology and Regulation (TechReg), housed at the Tilburg Institute for Law, Technology, and Society (TILT) at Tilburg University in the Netherlands.
Technology and Regulation (TechReg) is an international journal of law, technology and society, with an interdisciplinary identity. TechReg provides an online platform for disseminating original research on the legal and regulatory challenges posed by existing and emerging technologies (and their applications) including, but by no means limited to, the Internet and digital technology, artificial intelligence and machine learning, robotics, neurotechnology, nanotechnology, biotechnology, energy and climate change technology, and health and food technology. We conceive of regulation broadly to encompass ways of dealing with, ordering and understanding technologies and their consequences, such as through legal regulation, competition, social norms and standards, and technology design (or in Lessig’s terms: law, market, norms and architecture).
We aim to address critical and sometimes controversial questions such as:
- How do new technologies shape society both positively and negatively?
- Should technology development be steered towards societal goals, and if so, which goals and how?
- What are the benefits and dangers of regulating human behavior through technology?
- What is the most appropriate response to technological innovation, in general or in particular cases?
It is in this sense that TechReg is intrinsically interdisciplinary: we believe that legal and regulatory debates on technology are inextricable from societal, political and economic concerns, and that therefore technology regulation requires a multidisciplinary, integrated approach. Through a combination of monodisciplinary, multidisciplinary and interdisciplinary articles, the journal aims to contribute to an integrated vision of law, technology and society.
We invite original, well-researched and methodologically rigorous submissions from academics and practitioners, including policy makers, on a wide range of research areas such as privacy and data protection, security, surveillance, cybercrime, intellectual property, innovation, competition, governance, risk, ethics, media and data studies, and others.
TechReg is double-blind peer-reviewed and completely open access for both authors and readers. TechReg does not charge article processing fees.
When I can summon the energy to do so, I worry that the current unpleasantness threatens to exhaust my capacity for outrage.
However, I am now able to report that some news still shocks and surprises, such as this report from the ACLU, Citizens on Hold: A Look at ICE’s Flawed Detainer System in Miami-Dade County. It is not pretty reading.
Miami-Dade County’s records show that between February 2017 and February 2019, ICE sent the jail 420 detainer requests for people listed as U.S. citizens, only to later cancel 83 of those requests—evidently because the agency determined, after the fact, that its targets were in fact U.S. citizens. The remaining individuals’ detainers were not canceled, and so they continued to be held for ICE to deport them.
The ACLU reports that false detainer requests are fairly common across the country, but that we here in Miami are the epicenter.
Immigrant-rights groups say ICE’s databases are filled with outdated information. (And the ACLU says ICE’s data bases often fails to reflect that people become naturalized citizens.) And, ACLU notes a CNN investigation that showed ICE agents routinely forging their bosses’ signatures on critical detention warrants to skip mandatory document reviews.
Meanwhile, the State Legislature is considering a bill that would require local cops to honor ICE requests.
(Spotted via New Times, ICE Issued False Deportation Requests for 420 U.S. Citizens in Miami-Dade, ACLU Reports.)
I’m happy to report that the Arizona Law Review has published When AIs Outperform Doctors: Confronting the Challenges of a Tort-Induced Over-Reliance on Machine Learning, 61 Ariz. L. Rev. 33 (2019), that I co-authored with Ian Kerr (U. Ottawa) and Joelle Pineau (McGill U.).
Here’s the abstract:
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, effective ML could create overwhelming legal and ethical pressure to delegate the diagnostic process to the machine. Ultimately, a similar dynamic might extend to treatment also. If we reach the point where the bulk of clinical outcomes collected in databases are ML-generated diagnoses, this may result in future decisions that are not easily audited or understood by human doctors. Given the well-documented fact that treatment strategies are often not as effective when deployed in clinical practice compared to preliminary evaluation, the lack of transparency introduced by the ML algorithms could lead to a decrease in quality of care. This Article describes salient technical aspects of this scenario particularly as it relates to diagnosis and canvasses various possible technical and legal solutions that would allow us to avoid these unintended consequences of medical malpractice law. Ultimately, we suggest there is a strong case for altering existing medical liability rules to avoid a machine-only diagnostic regime. We argue that the appropriate revision to the standard of care requires maintaining meaningful participation in the loop by physicians the loop.
I think this is one of the best articles I’ve written or co-written–certainly in the top five. I’m particularly proud that I worked out, or intuited, a property of Machine Learning that was either not present or certainly not prominent in the literature: that if all the inputs to future generations of ML systems are due to the output of earlier generations of the ML system, there’s a chance it may all go wrong.
Reasonable people could disagree about the size of that chance, but if it happens at least with current technology there’s no way the system itself would warn us. Depending on the complexity of the system, and the extent to which doctors have been deskilled by the prevalence of the ML technology, we might be hard put to notice some types of degradation ourselves.
It would be good, therefore, to try to engineer legal rules that would make this possibly very unhealthy outcome much less likely.
Nicholas Terry of Indianan University was kind enough to ask me to join him and other experts on episode 151 (!) of his podcast, This Week in Health Law (TWIHL) which was devoted to AI and health care:
I am joined by Abbe Gluck, Professor of Law and the Faculty Director of the Solomon Center for Health Law and Policy at Yale Law School. In November 2018 her team pulled together an excellent roundtable on “The Law and Policy of AI, Robotics, and Telemedicine in Health Care.” This episode of TWIH is the first of two taking a deeper dive into just a few of the issues that were so well presented at the roundtable. Here we were joined by Michael Froomkin, the Laurie Silvers and Mitchell Rubenstein Distinguished Professor of Law at the University of Miami School of Law and by Nicholson Price, Assistant Professor of Law at The University of Michigan Law School. Topics ranged from consent in the next generation of healthcare research to data protection, and appropriate regulatory models.
Jotwell Blenderlaw