William_S

153Joined Oct 2014

Comments
47

One tool that I think would be quite useful is having some kind of website where you gather:

  1. Situations: descriptions of decisions that people are facing, and their options
  2. Outcomes: the option that they took, and how they felt about it after the fact

Then you could get a description of a decision that someone new is facing and automatically assemble a reference class for them of people with the most similar decisions and how they turned out. Could work without any ML, but language modelling to cluster similar situations would help.

Kind of similar information to a review site, but hopefully can aggregate by situation instead of by product used, and cover decisions that are not in the category of "pick a product to buy"

Appreciate that point that they are competing for time (as I was only thinking of monopolies over content).

If the reason it isn't used is that users don't "trust that the system will give what they want given a single short description", then part of the research agenda for aligned recommender systems is not just producing systems that are aligned, but systems where their users have a greater degree of justified trust that they are aligned (placing more emphasis on the user's experience of interacting with the system). Some of this research could potentially take place with existing classification-based filters.

While fully understanding a user's preferences and values requires more research, it seems like there are simpler things that could be done by the existing recommender systems that would be a win for users, ie. facebook having a "turn off inflammatory political news" switch (or a list of 5-10 similar switches), where current knowledge would suffice to train a classification system.

It could be the case that this is bottlenecked by the incentives of current companies, in that there isn't a good revenue model for recommender systems other than advertising, and advertising creates the perverse incentive to keep users on your system as long as possible. Or it might be the case that most recommender systems are effectively monopolies on their respective content, and users will choose an aligned system over an unaligned one if options are available, but otherwise a monopoly faces no pressure to align their system.

In these cases, the bottleneck might be "start and scale one or more new organizations that do aligned recommender systems using current knowledge" rather than "do more research on how to produce more aligned recommender systems".

If we want to maximize flow-through effects to AI Alignment, we might want to deliberately steer the approach adopted for aligned recommender systems to one that is also designed to scale to more difficulty problems/more advanced AI systems (like Iterated Amplification). Having an idea become standard in the world of recommender systems could significantly increase the amount of non-saftey researcher effort put towards that idea. Solving the problem a bit earlier with a less scalable approach could close off this opportunity.

I wonder how much of the interview/work stuff is duplicated between positions - if there's a lot of overlap, then maybe it would be useful for someone to create the EA equivalent of TripleByte - run initial interviews/work projects with a third party organization to evaluate quality, pass along to most relevant EA jobs.

I agree with this. It seems like the world where Moral Circle Expansion is useful is the world where:

The creators of AI are philosophically sophisticated (or persuadable) enough to expand their moral circle if they are exposed to the right arguments or work is put into persuading them.

They are not philosophically sophisticated enough to realize the arguments for expanding the moral circle on their own (seems plausible).

They are not philosophically sophisticated enough to realize that they might want to consider a distribution of arguments that they could have faced and could have persuaded them about what is morally right, and design AI with this in mind (ie CEV), or with the goal of achieving a period of reflection where they can sort out the sort of arguments that they would want to consider.

I think I'd prefer pushing on point 3, as it also encompasses a bunch of other potential philosophical mistakes that AI creators could make.

I don't think CEV or similar reflection processes reliably lead to wide moral circles. I think they can still be heavily influenced by their initial set-up (e.g. what the values of humanity when reflection begins).

Why do you think this is the case? Do you think there is an alternative reflection process (either implemented by an AI, by a human society, or combination of both) that could be defined that would reliably lead to wide moral circles? Do you have any thoughts on what would it look like?

If we go through some kind of reflection process to determine our values, I would much rather have a reflection process that wasn't dependent on whether or not MCE occurred before hand, and I think not leading to a wide moral circle should be considered a serious bug in any definition of a reflection process. It seems to me that working on producing this would be a plausible alternative or at least parallel path to directly performing MCE.

I've talked to Wyatt and David, afterwards I am more optimistic that they'll think about downside risks and be responsive to feedback on their plans. I wasn't convinced that the plan laid out here is a useful direction, but we didn't dig into it into enough depth for me to be certain.

Seems like the main argument here is that: "The general public will eventually clue in to the stakes around ASI and AI safety and the best we can do is get in early in the debate, frame it as constructively as possible, and provide people with tools (petitions, campaigns) that will be an effective outlet for their concerns."

One concern about this is that "getting in early in the debate" might move up the time that the debate happens or becomes serious, which could be harmful.

An alternative approach would be to simply build latent capacity - work on issues that are already in the political domain (I think basic income as a solution for technological employment is something that is already out there in Canada), but avoid raising new issues until other groups move into that space too. While you're doing that, you could build latent capacity (skills, networks) and learn how to effectively advocate in spaces that don't carry the same risks of prematurely politicizing AI related issues. Then when something related to AI becomes a clear goal for policy advocacy, moving onto it at the right time.

Thanks for the Nicky Case links

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