Of course some day, someone really will figure out how to use a robot to do a burglary. Or, more likely, subvert one via your smart home.
We’ll be talking about what robots are actually coming, what they may do, and how we should prepare for it, at We Robot 2019, which starts tomorrow. Advance registration is closed, but on-site registration will be available.
This evening I’m attending an event on “Blockchain: Business, Regulation, Law and the Way Forward” featuring Jerry Britto (Coin Center), Marcia Weldon (MiamiLaw), and Samir Patel (Holland & Knight).
The event is organized jointly by three student groups: the Federalist Society, the Business law Society, and the Alliance Against Human Trafficking. That’s a pretty eclectic group. I think it shows how widely the blockchain dream has taken hold.
And yet, despite this, not absolutely everyone loves blockchain. I for one am somewhat skeptical, as I think the use cases are much more limited than the optimists would have it. Indeed, my views are almost summarized by this great graphic, which sets out a decision tree for people thinking of using blockchain:
Yes, the reality is a bit more complicated, but if you can’t explain why the above doesn’t apply to you, you probably shouldn’t be using blockchain….
We have an action-packed lineup planned for We Robot 2019. The main conference is April 12-13, with an optional workshop day on April 11. I’ve put the schedule below; you should register now for We Robot 2019 if you haven’t already.
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.