Cameron Berg

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I think both our sets of results show that (at least) a significant minority believe that the community has veered too much in the direction of AI/x-risk/longtermism. 


But I don't think that either sets of results show that the community overall is lukewarm on longtermism. I think the situation is better characterised as division between people who are more supportive of longtermist causes (whose support has been growing), and those who are more supportive of neartermist causes.

It seems like you find the descriptor 'lukewarm' to be specifically problematic—I am considering changing the word choice of the 'headline result' accordingly given this exchange. (I originally chose to use the word 'lukewarm' to reflect the normal-but-slightly-negative skew of the results I've highlighted previously. I probably would have used 'divided' if our results looked bimodal, but they do not.) 

What seems clear from this is that the hundreds of actively involved EAs we sampled are not collectively aligned (or 'divided' or 'collectively-lukewarm' or however you want to describe it) on whether increased attention to longtermist causes represents a positive change in the community—despite systematically mispredicting numerous times that the sample would respond more positively. I will again refer to the relevant result to ensure any readers appreciate how straightforwardly this interpretation follows from the result—

(~35% don't think positive shift, ~30% do; ~45% don't think primary focus, ~25% do. ~250 actively involved EAs sampled from across 10+ cause areas.)

This division/lukewarmness/misalignment represents a foundational philosophical disagreement about how to go about doing the most good and seemed pretty important for us to highlight in the write-up. It is also worth emphasizing that we personally care very much about causes like AI risk and would have hoped to see stronger support for longtermism in general—but we did not find this, much to our surprise (and to the surprise of the hundreds of participants who predicted the distributions would look significantly different as can be seen above).

As noted in the post, we definitely think follow-up research is very important for fleshing out all of these findings, and we are very supportive of all of the great work Rethink Priorities has done in this space. Perhaps it would be worthwhile at some point in the future to attempt to collaboratively investigate this specific question to see if we can't better determine what is driving this pattern of results.

(Also, to be clear, I was not insinuating the engagement scale is invalid—looks completely reasonable to me. Simply pointing out that we are quantifying engagement differently, which may further contribute to explaining why our related but distinct analyses yielded different results.)

Thanks again for your engagement with the post and for providing readers with really interesting context throughout this discussion :)

Thanks for sharing all of this new data—it is very interesting! (Note that in my earlier response, I had nothing to go on besides the 2020 result you have already published, which indicated that the plots you included in your first comment were drawn from a far wider sample of EA-affiliated people than what we were probing in our survey, which I still believe is true. Correct me if I'm wrong!)

Many of these new results you share here, while extremely interesting in their own right, are still not apples-to-apples comparisons for the same reasons we've already touched on.[1] 

It is not particularly surprising to me that we are asking people meaningfully different questions and getting meaningfully different results given how generally sensitive respondents in psychological research are to variations in item phrasing. (We can of course go back and forth about which phrasing is better/more actionable/etc, but this is orthogonal to the main question of whether these are reasonable apples-to-apples comparisons.)

The most recent data you have that you mention briefly at the end of your response seems far more relevant in my view. It seems like both of the key results you are taking issue with here (cause prioritization and lukewarm longtermism views) you found yourself to some degree in these results (which it's also worth noting was sampled at the same time as our data, rather than 2 or 4 years ago):

Your result 1:

The responses within the Cause Prioritization category which did not explicitly refer to too much focus on AI, were focused on insufficient attention being paid to other causes, primarily animals and GHD.  

Our result 1:

We specifically find the exact same two cause areas, animals and GHD, as being considered the most promising to currently pursue. 

Your result 2 (listed as the first reason for dissatisfaction with the EA community):

Focus on AI risks/x-risks/longtermism: Mainly a subset of the cause prioritization category, consisting of specific references to an overemphasis on AI risk and existential risks as a cause area, as well as longtermist thinking in the EA community.

Our result 2:

We specifically find that our sample is overall normally distributed with a slight negative skew (~35% disagree, ~30% agree) that EAs' recent shift towards longtermism is positive.

I suppose having now read your newest report (which I was not aware of before conducting this project), I actually find myself less clear on why you are as surprised as you seem to be by these results given that they essentially replicate numerous object-level findings you reported only ~2 months ago. 

(Want to flag that I would lend more credence in terms of guiding specific action to your prioritization results than to our 'how promising...' results given your significantly larger sample size and more precise resource-related questions. But this does not detract from also being able to make valid and action-guiding inferences from both of the results I include in this comment, of which we think there are many as we describe in the body of this post. I don't think there is any strong reason to ignore or otherwise dismiss out of hand what we've found here—we simply sourced a large and diverse sample of EAs, asked them fairly basic questions about their views on EA-related topics, and reported the results for the community to digest and discuss.)

  1. ^

    One further question/hunch I have in this regard is that the way we are quantifying high vs. low engagement is almost certainly different (is your sample self-reporting this/do you give them any quantitative criteria for reporting this?), which adds an additional layer of distance between these results.

Hi Yanni, this is definitely an important consideration in general. Our goal was basically to probe whether alignment researchers think the status quo of rapid capabilities progress is acceptable/appropriate/safe or not. Definitely agree that for those interested, eg, in understanding whether alignment researchers support a full-blown pause OR just a dramatic slowing of capabilities progress, this question would be insufficiently vague. But for our purposes, having the 'or' statement doesn't really change what we were fundamentally attempting to probe. 

People definitely seem excited in general about taking on more multidisciplinary approaches/research related to alignment (see this comment for an overview).

Thanks for your comment! I would suspect that these differences are largely being driven by the samples being significantly different. Here is the closest apples-to-apples comparison I could find related to sampling differences (please do correct me if you think there is a better one):

From your sample:

From our sample:

In words, I think your sample is significantly broader than ours: we were looking specifically for people actively involved (we defined as >5h/week) in a specific EA cause area, which would probably correspond to the non-profit buckets in your survey (but explicitly not, for example, 'still deciding what to pursue', 'for profit (earning to give)', etc., which seemingly accounts for many hundreds of datapoints in your sample).

In other words, I think our results do not support the claim that 

[it] isn't that EAs as a whole are lukewarm about longtermism: it's that highly engaged EAs prioritise longtermist causes and less highly engaged more strongly prioritise neartermist causes.

given that our sample is almost entirely composed of highly engaged EAs.

Additional sanity checks on our cause area result are that the community's predictions of the community's views do more closely mirror your 2020 finding (ie, people indeed expected something more like your 2020 result)—but that the community's ground truth views are clearly significantly misaligned with these predictions. 

Note that we are also measuring meaningfully different things related to cause area prioritization between the 2020 analysis and this one: we simply asked our sample how promising they found each cause area, while you seemed to ask about resourced/funded each cause area should be, which may invite more zero-sum considerations than our questions and may in turn change the nature of the result (ie, respondents could have validly responded 'very promising' to all of the cause areas we listed; they presumably could not have similarly responded '(near) top priority' to all of the cause areas you listed).

Finally, it is worth clarifying that our characterization of our sample of EAs seemingly having lukewarm views about longtermism is motivated mainly by these two results:

These results straightforwardly demonstrate that the EAs we sampled clearly predict that the community would have positive views of 'longtermism x EA' (what we also would have expected), but the group is actually far more evenly distributed with a slight negative skew on these questions (note the highly statistically significant differences between each prediction vs. ground truth distribution; p≈0 for both).

Finally, it's worth noting that we find some of our own results quite surprising as well—this is precisely why we are excited to share this work with the community to invite further conversation, follow-up analysis, etc. (which you have done in part here, so thanks for that!).

Thanks for your comment! Agree that there are additional relevant axes to consider than just those we present here. We actually did probe geography to some extent in the survey, though we don't meaningfully include this in the write-up. Here's one interesting statistically significant difference between alignment researchers who live in urban or semi-urban environments (blue) vs. those who live everywhere else (suburban, ..., remote; red):

Agree that this only scratches the surface of these sorts of questions and that there are other important sources of intellectual/psychological diversity that we are not probing for here.

Good points and thanks for the question. One point to consider is that AISC publicly noted that they need more funding, which may have been a significant part of the reason that they were the most common donation recipient in the alignment survey. We also found that a small subset of the sample explicitly indicated they were involved with AISC (7 out of 124 participants). This is just to provide some additional context/potential explanation to what you note in your comment.

As we note in the post, we were generally cautious to exclude data from the analysis and opted to prioritize releasing the visualization/analysis tool that enables people sort and filter the data however they please. That way, we do not have to choose between findings like the ones you report about pause support x quantity of published work; both statistics you cite are interesting in their own right and should be considered by the community. We generally find though that the key results reported are robust to these sorts of filtering perturbations (let me know if you discover anything different!). Overall, ~80% of the alignment sample is currently receiving funding of some form to pursue their work, and ~75% have been doing this work for >1 year, which is the general population we are intending to sample.

Good find! Two additional points of context: 

  1. Alignment researchers from our sample broadly do support pausing or dramatically slowing AI development—
  2. We find in training a classifier to predict alignment researchers' answer to the above question (using their personality, values, moral foundations data as features), the single most important (ie, predictively relevant) feature is indeed their liberty moral foundation score. So it does seem like liberty generally mediates researchers' willingness to support pausing AI development.

Fair enough—I think we are trying to generally point at the distinction between 'raw intelligence' and other 'supplementary' cognitive traits that also predict effectiveness/success (like work ethic, people skills, etc.). This differentiation is the important takeaway, I think, rather than the exact terms we are using to point at it.

Hi Ben, we are continuing to accept further responses and of course value any additional respondents. Stopping donations is more a function of our available budget for this project than how much value we put on the additional data. We are keeping the form open until the data analysis is complete (it is easy to just plug in new entries to the existing analysis pipeline), at which point we will close the form. No specific deadline, but we imagine the analysis will be complete in the next week or two.

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