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.