"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.

Not surprising. Computer scientists have a bit of experience with computation, after all. It would actually be more concerning if a bunch of new stuff was being invented whole cloth.

> but it's sort of putting the cart before the horse.

Not at all. ML algorithms are just fucking algorithms and computer scientists have been thinking about what it means for an algorithm to be correct since... Turing.

And have been proving various theorems about ML algorithms in particular since at least the 60s.

Complaints that "AI safety looks a lot like previous CS research" are basically equivalent to observations that "neural nets have been around for a lot longer than alexnet".

> 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.

I disagree. This is how you end up with endless navel gazing about trolley problems while actual vehicles kill people by accelerating without control because redundant parts are too expensive and engineers don't have enough voice. Philosophers are rarely interested in honest-to-god engineering ethics, which almost always boils down to "pay well enough to hire good people, and then listen to the good people you're paying good money to have around".

Would you mind expanding on this? I would expect representatives of a field of knowledge that has an area called 'ethics' to be more concerned about ethics than your run of the mill engineer.

But the intention doesn't really matter.

What matters is the utility of the output!

In fact, somewhat ironically, I think a lot of the good work on ethics for AI is coming out of engineering, business, statistics, and economics departments. And those academic departments to do be a bit more "money grubbing" relative to philosophy :-)

I have an experience to share. I was there at the start of the web in the sense that I was building early web servers. I contributed nothing, but I was very excited and interested - as were tens of thousands of Computer Scientists.

I did not imagine any of the negative consequences of the web that have emerged. Very few people did. The community was blindsided, and I think that we have a reasonable excuse, just as a person t-boned on a junction has a reasonable excuse. We didn't see it coming, and it had never happened to us before.

With AI the fears and concerns raised in the media are mostly stupid, but there are genuine potential societal harms in terms of loss of freedom, dignity and opportunity that could easily arise if we don't set the system to favour human values over commercial and state ones.

We will not have the excuse of ignorance and surprise this time, and so I salute informed, reasonable and open people who are making the effort to engage with these issues.

The point of my post was that I did not foresee, nor did almost anyone, but we were idealistic and as a community we had not had the pervasive impact that Computer Science has now had on people's lives. We now have a far greater resource, experience and responsibility to innovate responsibly and with care. Delinquent and careless research that creates harm is unacceptable.

1. Someone with extensive knowledge of machine learning, and a much smaller (but existent) interest in the societal importance of interpretability

Or

2. A policy guy who knows tons about why these things should be interpretable, but has a very limited knowledge of machine learning?

Now, let’s say the position is policy relating to machine learning. Do you hire

1. The guy with extensive knowledge of policy who knows something of machine learning

Or

2. The guy with extensive knowledge of ML who knows a bit of policy?

It just seems like common sense, and it’s hard not to read a lot of comments as high ego engineers arguing that their tribe needs more sinecures and special treatment

I would hire both. If you want to do anything serious in the space, you need the resources to hire a well-rounded team. Maybe in five years this won't be true, but at the moment this is a space where technological capabilities are driving policy making decisions.

I had to choose one, the answer depends on if this is a soldier or a general.

If it's a soldier ($ or $$ salary), I would choose a CS PhD who has demonstrated an interest and aptitude for learning about policy. There are a lot of opportunities for that person to learn about policy including part-time fellowships with think tanks and federal agencies, sometimes even embedded in a lawmaker's staff. Conversely, taking a policy person and getting them to the point where they can adequately process the fire hose of AI fairness/safety/explainability research is going to require a lot more effort.

If it's a general ($$$ or $$$$ salary), I would choose whoever I could manage to hire that has the most influence in whatever agency or legislature is most relevant to the policies I want to push.

But again, especially for soldiers, it's a false choice, and if you find yourself in a situation where you have to make this choice then you need to focus on fundraising instead of your first hire...