Researchers propose a new interdisciplinary subfield called “Machine Behaviour”

I'm inclined to believe a substantial number of researchers are currently being deliberately fuzzy about what "AI" can and cannot do. Why not call it algorithms and statistics? I think lay-people have a very skewed understanding of what has already been achieved through AI. They may also not understand the word algorithms but at least it doesn't make them think of Skynet. For example, if you asked a person on the street whether there exists an "artificially intelligent supercomputer" somewhere that could help you plan all aspects of a small but entertaining dinner party, they would probably say yes. They imagine that you could just ask IBM Watson to help out, and he'd tell you what to do. This is completely false. "AI" systems are very fragile, and yes, we could build something that plans dinner parties, but we'd have to start over if we needed to plan a kid's birthday party instead. It's very far from strong AI. Ten years ago when it was mostly IBM telling lies, the end result was a couple of billion dollars wasted by hapless healthcare conglomerates. That's already bad. But now we have people from MIT, Stanford, Harvard, Yale, and more embracing the term AI and relying on unfounded hype to push for funding. It would be much less sexy if we called it "Facebook/Google/etc enable unfair/discriminatory advertising by combining intensive data collection with logistic regression, sometimes in multiple layers, and with some graph algorithms thrown in". But it would be a much better starting point for a well-informed debate. I'm not trying to minimize the importance of algorithms in our world, but a healthy discussion should be based on a sound understanding of the facts on the ground, and AI hype is not helping with that. I strongly prefer the less hyperbolic terminology adopted by someone like Aaron Roth at UPenn, e.g. see the blurb for "The Ethical Algorithm: The Science of Socially Aware Algorithm Design".

Bonus AI rant: For the celebration of the new MIT Schwarzman College of Computing, which is a huge expansion of the arguably most important computer science department in the world, there was a discussion panel on AI consisting of MIT President Reif, Henry Kissinger (War criminal?, Theranos board member, wannabe AI expert), Tom Friedman (columnist of limited substance), and Stephen Schwarzman (business man, coined the "increased taxes on carried interest are like Hitler's invasion of Poland" analogy, brought the dough). How the heck is that the inaugural panel!?!

Obviously none of these are AI and I don't see how you're helping anything by insinuating they are.

What has AI policy produced so far, really? Algorithm analysis and safety research is done on the algorithmic side, not on the policy side. Every time I have read an AI policy paper, it has been listings of desirable properties with absolutely no input on how to make any real steps towards them. In the meantime, realities of algorithm driven mass surveillance and censorship is moving ahead at a rapid pace.

Possible conclusion 1: We just need way more AI policy research.

Possible conclusion 2: AI policy is done by the wrong people and deep algorithmic understanding is a pre-requisite.

Possible conclusion 3: Current AI policy is just used as a fig-leaf by tech companies who hire a few policy essay-writers without substance. The lack of progress in that area is a feature, not bug.

Not mutually exclusive, not exhaustive. Feel free to point to valuable policy research.

I can see how a good-enough marriage of ML/NN to classic symbolic AI could yield a system capable of higher-order, intention-based “behavior” in complex circumstances. But we’re still a long way from achieving that, as far as I know.

"The correct way of ensuring algorithmic fairness is X" where X is something they already happened to work on.

In various papers X could be robust optimization, differential privacy, causal inference, adversarial training, etc. The papers are mostly good, actually, but it's sort of putting the cart before the horse.

I think there might be legitimate value in having a bunch of lawyers, sociologists, and philosophers set a target more-or-less in ignorance, and let the people on the technical side try to hit it.

Of course even better would be interdisciplinary collaboration but that's hard, there aren't many incentives in its favor (where do you publish? any given venue is worthless to half of the authors). and it requires humility on the part of everyone involved.