Thank you for compiling this list. This is useful, and I expect to point people to it in the future. The best thing, IMO, is that it is not verbose and not dripping with personal takes on the problem; I would like to see more compilations of topics like this to give other people a leg up when they aspire to venture into a field.
A potential addition is Dan Hendryck's PAIS agenda, in which he advocates for ML research that promotes alignment without also causing advances in capabilities. This effectively also slows AI (capabilities) development, and I am quite partial to this idea.
Yay.
Many other collections / reading lists exist, and I'm aware of many public and private ones in AI strategy, so feel free to DM me strategy/governance/forecasting topics you'd want to see collections on.
I haven't updated this post much since April but I'll update it soon and plan to add PAIS, thanks.
Thanks for writing this!
In addition to regulatory approaches to slowing down AI development, I think there is room for "cultural" interventions within academic and professional communities that discourage risky AI research:
https://www.lesswrong.com/posts/ZqWzFDmvMZnHQZYqz/massive-scaling-should-be-frowned-upon
This is a list of past and present research that could inform slowing AI. It is roughly sorted in descending order of priority, between and within subsections. I've read about half of these; I don't necessarily endorse them. Please have a low bar to suggest additions, replacements, rearrangements, etc.
There is little research focused on whether or how to slow AI progress.[1]
Relates to "Racing & coordination." Roughly, that subsection is about world-modeling and threat-modeling and this subsection is about solutions and interventions.
See generally Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims (Brundage et al. 2020).
There are some good lists and analyses, but not focused on slowing AI.
Racing for powerful AI, how actors that develop AI act, and how actors could coordinate to decrease risk. Understanding how labs act and racing for powerful AI seem to be wide open problems, as does giving an account of the culture of progress and publishing in AI labs and the ML research community.
I've read less than half of these; possibly many of them are off-point or bad.
There are no experts on slowing AI, but there are people who it might be helpful to talk to, including (disclaimer: very non-exhaustive) (disclaimer: I have not talked to all of these people):
There is also little research on particular relevant considerations, like how multipolarity among labs relates to x-risk and to slowing AI, or how AI misuse x-risk and non-AI x-risk relate to slowing AI.
I expect there is a small selection bias where the people who think and write about slowing AI are disposed to be relatively optimistic about it.