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.
Consent, that is ‘notice and choice,’ is a fundamental concept in the U.S. approach to data privacy, as it reflects principles of individual autonomy, freedom of choice, and rationality. Big Data, however, makes the traditional approach to informed consent incoherent and unsupportable, and indeed calls the entire concept of consent, at least as currently practiced in the U.S., into question.
Big Data kills the possibility of true informed consent because by its very nature one purpose of big data analytics is to find unexpected patterns in data. Informed consent requires at the very least that the person requesting the consent know what she is asking the subject to consent to. In principle, we hope that before the subject agrees she too comes to understand the scope of the agreement. But with big data analytics, particularly those based on Machine Learning, neither party to that conversation can know what the data may be used to discover.
I then go on to discuss the Revised Common Rule, which governs any federally funded human subjects research. The revision takes effect in early 2019, and it relaxes the informed consent rule in a way that will set a bad precedent for private data mining and research. Henceforth researchers will be permitted to obtain open-ended “broad consent”–-i.e. “prospective consent to unspecified future research”–-instead of requiring informed consent, or even ordinary consent, on a case-by-case basis. That’s not a step forward for privacy or personal control of data, and although it’s being driven by genuine public health concerns the side-effects could be very widespread.
One of my two summaries is online at Balkanization, Organizing the Federal Government’s Regulation of AI. In it I argue that most issues relating to medical AI shouldn’t be regulated separately from AI in general; there are real issues of policy but they’re complicated. A first step should be to set up a national think tank and coordination center in the White House that could advise both agencies and state and local governments.