(Just a comment on some of the above, not all)

Agreed and thanks for pointing out here that each of these resources has different content, not just presentation, in addition to being aimed at different audiences. This seems important and not highlighted in the post.

We then get into what we want to do about that, where one of the major tricky things is the ongoing debate of "how much researchers need to be thinking in the frame of xrisk to make useful progress in alignment", which seems like a pretty important crux, and another is "what do ML researchers think after consuming different kinds of content", where Thomas has some hypotheses in the paragraph "I'd guess..." but we don't actually have data on this and I can think of alternate hypotheses, which also seems quite cruxy.

Or in other words, we can't get them to accept conclusions we favor, but we can frame alignment in such a way that it just seems natural.

Thanks! Quick responses:

I think these results, and the rest of the results from the larger survey that this content is a part of, have been interesting and useful to people, including Collin and I. I'm not sure what I expected beforehand in terms of helpfulness, especially since there's a question "helpful with respect to /what/", and I expect we may have different "what"s here.

Good to know. When discussing some recent ideas I had for surveys, several people told me that their survey results underperformed their expectations, so I was curious if you would say the same thing.
 

Yeah, I think this is currently mostly done informally -- when Collin and I were choosing materials, we had a big list, and were choosing based on shared intuitions that EAs / ML researchers / fieldbuilders have, in addition to applying constraints like "shortness". Our full original plan was also much longer and included testing more readings -- this was a pilot survey. Relatedly, I don't think these results are very surprising to people (which I think you're alluding to in this comment) -- somewhat surprising, but we have a fair amount of information about researcher preferences already.

Thanks for explaining. I realise that the point of that part of my comment was unclear, sorry. I think that using these sorts of surveys to test if best practice contrasts with current practice could make the findings clearer and spur improvement/innovation if needed. 

For instance, doing something like this: "We curated the 10 most popular public communication paper from AI Safety organisations and collected predictions from X public AI Safety communicators about which of thse materials would be most effective at persuading existing ML researchers to care about AI Safety. We tested these materials with a random sample of X ML researchers and [supported/challenged existing beliefs/practices]... etc." 

I am interested to hear what you think of the idea of testing using these sorts of surveys to test if best practice contrasts with current practice, but ok if you don't have time to explain! I imagine that it does add some extra complexity and challenge to the research process, so may not be worth it.

I hope you can do the larger study eventually. If you do, I would also like to see how sharing readings compares against sharing podcasts or videos etc. Maybe some modes of communication perform better on average etc.

Instead of contacting a random subset of people who had papers accepted at ML conferences? I think it sort of depends on one's goals here, but could be good. A few thoughts: I think this may already exist informally, I think this becomes more important as there's more people doing surveys and not coordinating with each other, and this doesn't feel like a major need from my perspective / goals but might be more of a bottleneck for yours!

Thanks, that's helpful. Yeah, I think that the panel idea is one for the future. My thinking is something like this: Understanding why and how AI Safety related materials (e.g., arguments, research agendas, recruitment type messages etc) influence ML researchers is going to become increasingly important to a growing number of AI Safety community actors (e.g., researchers, organisations, recruiters and movement builders). 

Whenever an audience becomes important to some social/business actor (e.g., government/academics/companies), this usually creates sufficient demand to justify setting up a panel/database to service those actors. Assuming the same trend, it may be important/useful to create a panel of ML researchers that AI Safety actors can access. 

Does that seem right?

I mention the above in part because I think that you are one of the people who might be best-placed to set something like this up if it seemed like a good idea. Also, because I think that there is a reasonable chance that I would use a service like this within the next two years and end up referring several other people (e.g., those producing choosing educational materials for relevant AI Safety courses) to use it.