Category Archives: Writings

New Paper–“Big Data: Destroyer of Informed Consent”

Just posted: A near-final draft of my latest paper, Big Data: Destroyer of Informed Consent. It will appear later this year in a special joint issue of the Yale Journal of Health Policy, Law, and Ethics and the Yale Journal of Law and Technology.

Here’s the tentative abstract (I hate writing abstracts):

The ‘Revised Common Rule’ took effect on January 21, 2019, marking the first change since 2005 to the federal regulation that governs human subjects research conducted with federal support or in federally supported institutions. The Common Rule had required informed consent before researchers could collect and use identifiable personal health information. While informed consent is far from perfect, it is and was the gold standard for data collection and use policies; the standard in the old Common Rule served an important function as the exemplar for data collection in other contexts.

Unfortunately, true informed consent seems incompatible with modern analytics and ‘Big Data’. Modern analytics hold out the promise of finding unexpected correlations in data; it follows that neither the researcher nor the subject may know what the data collected will be used to discover. In such cases, traditional informed consent in which the researcher fully and carefully explains study goals to subjects is inherently impossible. In response, the Revised Common Rule introduces a new, and less onerous, form of “broad consent” in which human subjects agree to as varied forms of data use and re-use as researchers’ lawyers can squeeze into a consent form. Broad consent paves the way for using identifiable personal health information in modern analytics. But these gains for users of modern analytics come with side-effects, not least a substantial lowering of the aspirational ceiling for other types of information collection, such as in commercial genomic testing.

Continuing improvements in data science also cause a related problem, in that data thought by experimenters to have been de-identified (and thus subject to more relaxed rules about use and re-use) sometimes proves to be re-identifiable after all. The Revised Common Rule fails to take due account of real re-identification risks, especially when DNA is collected. In particular, the Revised Common Rule contemplates storage and re-use of so-called de-identified biospecimins even though these contain DNA that might be re-identifiable with current or foreseeable technology.

Defenders of these aspects of the Revised Common Rule argue that ‘data saves lives’. But even if that claim is as applicable as its proponents assert, the effects of the Revised Common Rule will not be limited to publicly funded health sciences, and its effects will be harmful elsewhere.

This is my second foray into the deep waters where AI meets Health Law. Plus it’s well under 50 pages! (First foray here; somewhat longer.)

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‘When AIs Outperform Doctors’ Published

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.

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New Paper ‘When AIs Outperform Doctors’

My latest (draft!) paper, When AIs Outperform Doctors: The dangers of a tort-induced over-reliance on machine learning and what (not) to do about it is, I hope, special. I had the good fortune to co-author it with Canadian polymath Ian Kerr, and with Joëlle Pineau, one of the world’s leading machine learning experts.

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 longer 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. In time, effective machine learning 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 decision scenarios 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 real clinical practice compared to preliminary evaluation, the lack of transparency introduced by the ML algorithms could lead to a decrease in quality of care. The 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 in order to avoid a machine-only diagnostic regime. We argue that the appropriate revision to the standard of care requires the maintenance of meaningful participation by physicians in the loop.

I hope that it will be of interest to to lawyers, doctors, computer scientists, and a range of medical service providers and policy-makers. Comments welcome!

Posted in AI, Sufficiently Advanced Technology, Writings | 1 Comment

Latvian ‘Ocean’

My article Flood Control on the Information Ocean: Living With Anonymity, Digital Cash, and Distributed Databases has been translated into Latvian as “Plūdu kontroli par informāciju, kas okeānā: dzīvo ar anonimitāti, digitālās naudas, un sadalītās datu bāzes” by Arija Liepkalnietis.

Thank you Ms. Liepkalnietis! (But please add a Creative Commons License.)

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Personal Notes

Recently I’ve been posting pretty much nothing but videos. There two reasons. First, to the limited extent I’ve had spare time, I’ve been spending it reading political news with more horror than inspiration. Second, there hasn’t been much spare time: I just sent in a revised first draft of an article that will in due course be published by the Arizona Law Review. Meanwhile I also did a first draft of a conference proposal on some AI issues to send to a co-author. And, last week I discovered that the other conference proposal, on a privacy issue, that I thought was due at the end of this week turns out to be in fact a call for finished papers. Fortunately, I’d already started, but getting to the finish line is taking most of my time at present and seems likely to do so through the coming weekend. After that, another large privacy-related project looms in the distance. Meanwhile, of course, Jotwell is humming along. We recently started a new Contracts section.

I just had to turn down an invitation to speak on AI and robotics in Hong Kong next month because I couldn’t find enough times for all the makeup classes on such short notice. That hurt, as I’d have loved to go. But if you were maybe thinking of asking me to come talk about the exciting things I’m working on, some of which I’ll blog about when they are a little more baked, please be aware I’m on writing leave next semester, and I will have much more flexibility than usual–and, I hope, more work product too.

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Naming Files

Just modified my writing tips for students to add “A Few Words About File Names”:

Most of our students will spend at least some part of their careers working in large organizations where they share electronic documents. Students should get in the habit of giving their documents good file names. Thus “paper draft” is always bad — imagine if everyone calls their documents that!

Some organizations have naming conventions, but if not, be sure the file name has three things: your surname (if the primary author, so you get the credit), something substantive (not “paper” or “motion” but “Jones-dismiss-motion” or whatever), and a version number. You usually do not need a date, the software will do that automatically. For papers it’s probably a good idea to put your surname first, then a summary form of the paper title, as that way over the course of time all your submissions to your professor will be grouped together and easy to find.

It is good a habit to avoid spaces in file names (use – or _) so that files translate well to UNIX systems if they ever touch one. Do not use any other odd non-alphanumeric characters as it can mess up some older file systems.

All fairly obvious, but you would be amazed how many files I get from students called “paper”.

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