I think there is not a single EA organization I would consider unbiased on this question, including ourselves (despite our ongoing efforts not to be). That is exactly why we publish so much of our methodology and our assumptions openly. One of the main motivations for this work is concern about the effect of bias when assumptions and models are implicit or hidden. We would welcome more experts with broader backgrounds being involved in drafting and improving these estimates, which is part of what we hope this kind of public methodology enables.
On conflicts of interest: we aim to be transparent about potential or perceived conflicts of interest, and we state all present COIs in the post above and on the website (here and here), and we will update these when and if they change. RP is recommending these grants independently and no one outside of RP was involved in choosing the funds for inclusion.* You are welcome to review our methodology on how the funds were chosen and provide critique.
On team composition and priorities specifically, a few points: First, the researchers you list are not, in fact, the principal contributors to the project and it’s harder to pin us down even if you stuck to that methodology. For example, Carmen van Schoubroeck was the project lead for all this work, and started her EA career as a global health and development researcher. Some of the specific inputs that in theory could be biased here (like the choice of aggregation methods) are mine and my background involves starting a global health charity, being an animal welfare fund manager, and incubating and providing operational support to a large number of AI projects. And obviously RP works across all of these areas because I, personally, think it’s a good idea. Secondly, Claude does not have access to the work our researchers have done that’s not published, nor what they actually prioritize in their own giving. Third, our global health and development researchers gave feedback on the modeling assumptions, in the same way we involved researchers with other expertise on other cause areas. Having that option is one of the benefits of being a research organization with teams across causes.
More broadly, I would not consider Claude’s opinions about the priorities of our researchers to be a good method of gauging the quality of our work. I think all of the following:
The old adage that “personnel is policy” is relevant to any exercise that’s as complex as this one
I’m confident that there are ways to improve this work, and specific improvements to be gained by bringing in diverse perspectives (I’d be happy to discuss options here in more detail)
Despite (1) and (2) there are larger areas of improvement or concerns about this work than trying to infer cause area orientation from public work of staff and use that to infer potential bias in our choices for the model
Specifically with regard to (1), I can never tell you with certainty no one who worked on this was not subtly unconsciously biased, but one of the reasons people were chosen to work on this project at all is because of my perception of their lack of bias. This is a considerable filter to working on any cross cause work at RP and I stand behind the credibility of every person who worked on this was doing their best to be unbiased. I think this filtering combined with the process we had of having multiple people review work and getting input from funds and cause area experts should lead to less bias. I could be wrong, of course, but I think it would be more productive to make specific critiques of our work and choices because the specific choices are there to see. It’s not costless in time to do this, so I don’t begrudge anyone for not engaging, but you don’t have to infer if you think our animal moral weights are too high, or the cost-effectiveness of an area is too low, you can see what we chose and say what you think is wrong and why.
I truly wish for myself and RP to do the impartial good but we only ever achieve doing so imperfectly. But, primarily, the way we find out is through specific things we get wrong. So, if you have specific issues with inputs or choices that you think privileged one area over others, please point them out. We’d be happy to revise them if they should be improved. The current model surely isn’t perfect (as we acknowledge) and can benefit from specific, thoughtful criticism about our methodology, which we’ve described and linked to on this site.
*Despite the complete lack of involvement of former staff in our decisionmaking, in general I favor more transparency in EA so ill note you are correct that RP has done research on lead exposure before, and in fact, two former RP staff now work on the Lead Exposure Action Fund. I think our ability to model lead was improved because we’d done work on it and this was a pro for us including it, as we felt more confident we could accurately do so by the time of launch. I was also a fund manager for the EA Animal Welfare Fund. We also have former staff at Longview, and some at other parts of Coefficient Giving unrelated to LEAF. We also have former staff or board members at a number of the funds we list that we plan to consider including Astralis, the AI Safety Tactical Opportunities Fund, Giving Green, and Macroscopic Ventures.
Thanks Marcus for the reply. First this criticism is specifically about potential preferences and bias of the researchers. With a project like this with perhaps hundreds of junctures which require subjective decisions, I think that’s a reasonable discussion to have. I don’t think it’s fair to ask me shift the ground and ask to discuss the research methodology, I’m sure there’ll be plenty of great discussion about that. My point was purely concerns about potential researcher bias.
I completely agree with your 3 points, and specifically that there are larger areas of concern than the cause area orientation of researchers—but I still think it’s somewhat important .
This comment seems to twist the point I was trying to make “More broadly, I would not consider Claude’s opinions about the priorities of our researchers to be a good method of gauging the quality of our work” I was not questioning the quality of your work, I think your work is extremely high quality. Yet even the highest quality work can go in different directions, based on the assumptions and biases of the researchers who generate it. In many imprecise fields like development, economics and political science, the highest quality researchers can argue opposite sides. Stiglitz and Friedman both produced high quality work which often ended up at almost opposite conclusions. I would put cross-cause categorisation in a similar-ish category due to the wealth of assumptions required and uncertainties to be reckoned with. So in research like these yes, I think the make-up of the research team is important and open to scrutiny outside of objective criticism of the work itself. You might disagree with me on this one. Assuming people’s backgrounds is open for discussion, I think a Claude analysis of people’s previous work is reasonable if obviously very imprecise and yes potentially misleading.
I could be wrong here, but I feel like I may have been baited-and-switched a little on who are the major contributors here.. I went to your website and just searched the cross-cause prioritisation team, finding a page with photos and bios. You’ve said that the researchers I listed were not the principal researchers - then who were? That Carmen van Schoubroeck led the work isn’t listed anywhere I don’t think.
I agree with the direction but not the strength of this comment “I can never tell you with certainty no one who worked on this was not subtly unconsciously biased” and agree with your opening statement more “I think there is not a single EA organization I would consider unbiased on this question, including ourselves” We are all biased, often more than we think. Most of us (myself included) have a cess-pit of opinions and angles, despite our best efforts to the contrary. I think within EA we often overrate how objective we are, and we can only have so much objectivity based on our past experience and worldview built up over time. This is why I think for a cross-cause prioritisation exercise, it is helpful to start with a team with a wide range of prior opinions or perhaps very uncertain ones.
As a reference, I got to this from CCF page --> support our work --> under the “our team” section. Notably, the link is from this text:
“The Rethink Priorities Cross-Cause Fund sits on top of work done by our Worldview Investigations Team (WIT), and Interdisciplinary Research Team, groups established specifically to tackle the hard questions that most donors don’t have time to engage with: how to compare welfare across species, how to reason under deep uncertainty, how to weigh present benefits against future ones, how to aggregate competing moral views into a single allocation.
The team brings together training in philosophy, economics, statistics, cognitive science, moral psychology, and decision theory . The fund’s allocations also draw on the in-house expertise of RP’s Global Health and Development, Animal Welfare, AI departments, so the cross-cause model is informed by researchers working directly in each area, not just secondary literature. ”
So I’m interpreting that paragraph as saying that WIT work goes into the report, but not necessarily that the WIT team did all the work (in particular, the interdisciplinary research team clearly was also involved, and the second paragraph suggests other teams contributed as well).
Thanks so much @mal_graham🔸 that’s the web page appreciate that! I understand that the WIT doesn’t do all the work, but I think it was reasonable for me to assume that they were the major contributors given that they have been the team publishing the cross-cause work up to now, and were the team linked from the page.
Hey Nick, I don’t know if we are that far apart on the conceptual issue of potential bias but I think we are approaching this differently.
However, I really like the Stieglitz and Friedman analogy and think it is useful in many ways. Simply, there are lots of decision points here. And I really wish there were some other groups working on building such work so we could compare and contrast our work with theirs like you can in that context. If that were so, I think this type of meta-debate would be either less likely to occur, or more productive because we could point to more specific things.
At the same time, I think “count up the cause area backgrounds of the staff who worked on this” is misleading even if I agreed with the characterization of our staff, and I don’t. This is for a number of reasons and, if you’d permit, I’ll largely extend the economics and social science analogy to raise my points.
First, in some simple sense, I think looking at our staff’s background like this is like looking at the subfields GiveWell’s staff worked in prior to joining and if a disproportionate number worked on malaria using that to argue this is evidence of potential bias for why multiple of their top donation opportunities overall involve malaria. It’s not that this bias is not possible, it’s just a very blunt guide, at best, and the causal arrow may point the other way. That is, GiveWell may employ a lot of people who have backgrounds in malaria to help create their final estimates because they are particularly well suited to the task and/or malaria is an important area they have to cover.
Second, for this model many of the relevant choice points come from fields that have less ideological valence on the cause area lines (i.e. what do you think of risk attitudes) and potential bias in those areas is likely just as relevant to reaching conclusions as thinking at the level of cause area. Further, some inputs have effects difficult to pin down in the overall model, which limits the ability of people to even implicitly put their thumb on the scale because they don’t know what changing that input would change in the result. There are a few areas where it is obvious (i.e. make the animal moral weights higher or lower, reduce the cost-effectiveness of a given area) but those are the areas precisely where anyone looking at our model can see what we choose and object if they disagree.
But suppose you were convinced that Stiglitz tends to be biased in a liberal direction, and Friedman in a conservative one. What’s the best way to demonstrate that? I think it would often be to point to a specific assumption or choice they made that is questionable. This is why I asked you to point to something specific that you think is wrong. Not because I think it can’t be the case that we’re biased or that it’s inherently illegitimate to bring up the possibility, but because it’s the specifics that demonstrate that we are biased. I really do agree that too often some people in EA think everything they do is objective. I wrote this just last month and stand behind this as applying to basically everyone:
As Keynes observed about a parallel dynamic in economics: “practical men, who believe themselves to be quite exempt from any intellectual influences, are usually the slaves of some defunct economist.” The same applies here. Views that people describe as mere intuitions or common sense are often downstream of philosophical ideas promoted in their environments. When someone thinks such intuitions and implicit stances are indisputable or obviously correct, it’s more often than not evidence they haven’t examined or tested those ideas at all.
If someone doesn’t have the time to investigate an area, it can be at times reasonable to check for this kind of bias and discount appropriately (I think this is most useful as a guide when a group or individual has shown repeated bias in the past) but I also think this can at times serve as a shortcut to dismissing anything that doesn’t already ideologically align with yourself. Balancing these two competing forces can be a constant struggle, particularly in areas outside of your expertise and I know it’s something constantly in my mind when I read about politics.
Finally, to continue the analogy with economics, often the field of experts are debating a much more narrow range of opinions than exists in the public debate. For example, in immigration the economist debate about the impact of immigration on wages and employment is far more narrow than the general public discussion of the same topic. Stiglitz and Friedman (assuming you mean Milton Friedman) might have disagreed a lot but it was often within a largely shared framework. I’m pretty sure neither of them thought mass central planning would work, or that tariffs were largely paid by the exporting countries. I think many of the opinions people give about cause prioritization may fall into this category of something very few who think about the topic carefully dare to defend but, because those outside haven’t put in the time, they are unaware of this (in some sense through no fault of their own, not everyone can be an expert or very informed about every topic).
I think this type of distinction is likely to hold among serious efforts to build cross cause models.
Thanks there’s some insightful stuff here. I really appreciated this. “If someone doesn’t have the time to investigate an area, it can be at times reasonable to check for this kind of bias and discount appropriately (I think this is most useful as a guide when a group or individual has shown repeated bias in the past) but I also think this can at times serve as a shortcut to dismissing anything that doesn’t already ideologically align with yourself. Balancing these two competing forces can be a constant struggle, particularly in areas outside of your expertise and I know it’s something constantly in my mind when I read about politics.”
Despite my best efforts, I’ve really struggled to understand arguments around the nature of consciousness and valenced states. I’m still not entirely sure that any consciousness model is very meaningful but that’s another discussion… This means my analysis has often stepped up the level to process. As a side note I still think far more EA funds should go to animal welfare than currently does, although the defensive nature of responses from RP and other animal welfare folk has updated me a bit against this over the last couple of years.
I think the GiveWell analogy is a great one, and it would be fair to look at the backgrounds of staff that were doing prioritisatoin. If most came from malaria backgrounds I would be quite concerned, I haven’t looked into that. In a cross-cause prioritisation process I think there is more room for bias than in a CEA.
I think we’ll probably have to agree to disagree on research team make-up being open to outside scrutiny. Like you say I don’t think it’s the most important thing but still important,. You’ve said that “personel is policy” carries weight, but haven’t suggested how we should approach examining that? My Claude ranking method isn’t the best for sure, what other way you would suggest? From an RP organisational perspective, I think there is a risk that the org’s work could be undermined somewhat by people who might feel like a bunch of animal welfare/GCR folks might have disproportionate sway over a cross-cause prioritisation process like this.
I agree it would be great to have other groups working on this stuff, a poll a while ago was overwhelmingly in favour. The reality is though, you guys are it for now. There aren’t other groups working on moral weights and cross-cause prioritisation. I think this means that there’s perhaps more responsibility for balance within the organisation. The situation is more “Aristotle” dominating philosophy than Friedman and Stiglitz. I think with our current levels of information, thre is far more shared ground in economics than in cross-cause prioritisatoin. Perhaps within RP there’s much agreement but I would argue there will be a heavy “groupthink” element built there over time. That there are 10+ moral theories in this model illustrates the extreme diversity. Between animal welfare and human welfare we have a bridge of one moral weights project? While GCR uncertainties are far bigger still, with moral and practical junctures going almost uncountable. On the GCR front even something as important and straightforward as as “have those who worked on AI risk done ill or good so far” is hotly debated.
As a final (if a bit sour) note, RP as I’ve experienced it on the forum has seemed pretty impervious to criticism and suggestion. Responses are always intelligent and very well reasoned, but l haven’t seen openness to mind-changing, and not just in response to my comments. When criticism comes in, the response is polite well reasoned refutation—very good arguments for maintaining status-quo. I don’t think I’ve seen a response along the lines of “hey you have a good point there, let’s look into that” or even “yes that decision was tricky and we did X because...”. As an example I put a big effort into understanding the moral weights project (for personal interest reasons). Then after writing a decent post about the MWP process, the response from RP members was excellent and well thought through, but never acknowledged that any of my process points might be reasonable. In contrast orgs like GiveWell in my experience are far more open to mind changing.
I’m headed off to two weeks of conferencing in in a few hours so very likely won’t respond further after this but I do want to say a few things.
I take very seriously that you (or anyone else) believes we’ve come off as defensive or not open to changing our mind. I definitely think we haven’t always lived up to our communication ideals and could stand do better. I’m really sorry if you don’t think we aren’t open-minded but I don’t think this is true:
When criticism comes in, the response is polite well reasoned refutation—very good arguments for maintaining status-quo. I don’t think I’ve seen a response along the lines of “hey you have a good point there, let’s look into that” or even “yes that decision was tricky and we did X because...”.
I don’t have the time to do a throughout search and systematic weighing but I believe this comment section contains at least one example to the contrary. I think just a few comments down someone raises questions about what went into the AI discount and I stated why it’s shaky, what changing it does to the output, and how I hope to see it improve.
You’ve said that “personel is policy” carries weight, but haven’t suggested how we should approach examining that? My Claude ranking method isn’t the best for sure, what other way you would suggest? From an RP organisational perspective, I think there is a risk that the org’s work could be undermined somewhat by people who might feel like a bunch of animal welfare/GCR folks might have disproportionate sway over a cross-cause prioritisation process like this.
I’m not really sure what the best method is as a I said I think doing this type of counting is a very blunt, limited tool. From my experience, the worry about our staff and outcome bias basically cuts all directions as some GCR people consider us too animal friendly, some animal people consider us too GCR and GHD friendly, and some GHD people (like yourself) consider us too animal and GCR friendly. I have personally heard all of these concerns over the past several months while working on this project. This doesn’t invalidate any of the concerns (we really could be biased one of these ways, not all concerns are equally valid) but I think it can bring some perspective to it. I think one inherent limitation to this enterprise is we typically get staff who are willing to work on multiple areas and often have explicitly done so prior to joining RP. But that typically means few if any are perceived from the outside as a true “member” of any cause area.
But I think an additional reason it’s not super useful to assess our staff this way and why I still lean away from the utility of type of exercise, is the actual projects RP does are reflective most of all of what projects we think are valuable. WIT in particular, has done roughly four large projects and those projects were about moral weight, risk and uncertainty in giving, quantitative tools for cross cause prioritization, and digital minds. But the reason we selected those projects—which I believe are then counted as part of what cause areas they’ve focused on in the Claude search—is because we thought there was a useful contribution to make that we could do well. To put it mildly, I think there’s far less big intellectual gaps in the GHD space than in AW and GCR space, or in cause prioritization itself. So, that we filled in gaps outside of GHD that we think are needed to improve cause prioritization may be being used as evidence we don’t care about GHD.
Outside of general competence, here’s what I actually prioritize on staff working on cause prio projects (not necessarily in this order):
Willingness and ability to be truthful to the arguments and the facts
Willingness to seek out and engage alternative views, and get expert feedback
Philosophical/empirical knowledge and breadth of experience working on the specific types of problems at hand
Of these I think (3) is partially visible from the outside, and under certain circumstances (2). (1) can be seen in what choices and arguments are actually advanced. But none of this maps very well onto “cause area” background, and largely I think different types of methodological background and experience have more to do with the output that area inclination. (I also don’t think cause areas are the relevant level of crux, cause areas contain bundles of interventions that are extremely heterogenous but that’s a story for another day).
Perhaps within RP there’s much agreement but I would argue there will be a heavy “groupthink” element built there over time. That there are 10+ moral theories in this model illustrates the extreme diversity.
I actually want more. I think we didn’t capture everything I think is relevant about moral theories and hope to do better on this in the future. We had to start somewhere though.
I agree it would be great to have other groups working on this stuff, a poll a while ago was overwhelmingly in favour. The reality is though, you guys are it for now. There aren’t other groups working on moral weights and cross-cause prioritisation. I think this means that there’s perhaps more responsibility for balance within the organisation. The situation is more “Aristotle” dominating philosophy than Friedman and Stiglitz. I think with our current levels of information, thre is far more shared ground in economics than in cross-cause prioritisatoin.
I get that we are largely it for now and take this responsibility very seriously. Suffice it to say this project has dominated my thinking for months, and my desire to do the best we can with our initial offering has been my primary concern for several months. Hopefully despite your concerns, future editions of this project and others can do more to win your trust and live up to what we need to be be if we’re going to be the only game in cause prioritization town.
I think it would be more productive to make specific critiques of our work and choices because the specific choices are there to see. It’s not costless in time to do this, so I don’t begrudge anyone for not engaging, but you don’t have to infer if you think our animal moral weights are too high, or the cost-effectiveness of an area is too low, you can see what we chose and say what you think is wrong and why.
Hey Vasco, I replied to the last link here and I don’t have anything to add to Laura’s responses for your first two links.
In brief, the Donor Compass is streamlined, if you want more subtlety you probably should be using the advanced version of the tool. And I while I think we should aim to have secondary effects of interventions, I want to do it in a way that doesn’t unnecessarily penalize/reward areas where that data is/is not available, and to not have the effects of all interventions be dominated by deeply uncertain secondary impacts.
I think we should aim to have secondary effects of interventions
Great to know. Do you have any concrete plans or timelines? If not, how much funding would you need?
Nitpick. I would not call effects on non-target individuals “secondary”. I think they may be much larger than those on the target individuals (in expectation), and “secundary” makes it sound like they are less important to consider.
I want to do it in a way that doesn’t unnecessarily penalize/reward areas where that data is/is not available
and to not have the effects of all interventions be dominated by deeply uncertain secondary impacts
What if, given reasonable moral and empirical views, we should in fact be very uncertain about whether practically any intervention is better or worse than nothing accounting for effects on non-target individuals? I think this would be useful to know. The model should not force the conclusion that interventions which have historically been supported by the effective altruism community (like saving human lives, and cage-free egg campaigns) are better than nothing for all reasonable moral and empirical views?
Hi Nick,
I think there is not a single EA organization I would consider unbiased on this question, including ourselves (despite our ongoing efforts not to be). That is exactly why we publish so much of our methodology and our assumptions openly. One of the main motivations for this work is concern about the effect of bias when assumptions and models are implicit or hidden. We would welcome more experts with broader backgrounds being involved in drafting and improving these estimates, which is part of what we hope this kind of public methodology enables.
On conflicts of interest: we aim to be transparent about potential or perceived conflicts of interest, and we state all present COIs in the post above and on the website (here and here), and we will update these when and if they change. RP is recommending these grants independently and no one outside of RP was involved in choosing the funds for inclusion.* You are welcome to review our methodology on how the funds were chosen and provide critique.
On team composition and priorities specifically, a few points: First, the researchers you list are not, in fact, the principal contributors to the project and it’s harder to pin us down even if you stuck to that methodology. For example, Carmen van Schoubroeck was the project lead for all this work, and started her EA career as a global health and development researcher. Some of the specific inputs that in theory could be biased here (like the choice of aggregation methods) are mine and my background involves starting a global health charity, being an animal welfare fund manager, and incubating and providing operational support to a large number of AI projects. And obviously RP works across all of these areas because I, personally, think it’s a good idea. Secondly, Claude does not have access to the work our researchers have done that’s not published, nor what they actually prioritize in their own giving. Third, our global health and development researchers gave feedback on the modeling assumptions, in the same way we involved researchers with other expertise on other cause areas. Having that option is one of the benefits of being a research organization with teams across causes.
More broadly, I would not consider Claude’s opinions about the priorities of our researchers to be a good method of gauging the quality of our work. I think all of the following:
The old adage that “personnel is policy” is relevant to any exercise that’s as complex as this one
I’m confident that there are ways to improve this work, and specific improvements to be gained by bringing in diverse perspectives (I’d be happy to discuss options here in more detail)
Despite (1) and (2) there are larger areas of improvement or concerns about this work than trying to infer cause area orientation from public work of staff and use that to infer potential bias in our choices for the model
Specifically with regard to (1), I can never tell you with certainty no one who worked on this was not subtly unconsciously biased, but one of the reasons people were chosen to work on this project at all is because of my perception of their lack of bias. This is a considerable filter to working on any cross cause work at RP and I stand behind the credibility of every person who worked on this was doing their best to be unbiased. I think this filtering combined with the process we had of having multiple people review work and getting input from funds and cause area experts should lead to less bias. I could be wrong, of course, but I think it would be more productive to make specific critiques of our work and choices because the specific choices are there to see. It’s not costless in time to do this, so I don’t begrudge anyone for not engaging, but you don’t have to infer if you think our animal moral weights are too high, or the cost-effectiveness of an area is too low, you can see what we chose and say what you think is wrong and why.
I truly wish for myself and RP to do the impartial good but we only ever achieve doing so imperfectly. But, primarily, the way we find out is through specific things we get wrong. So, if you have specific issues with inputs or choices that you think privileged one area over others, please point them out. We’d be happy to revise them if they should be improved. The current model surely isn’t perfect (as we acknowledge) and can benefit from specific, thoughtful criticism about our methodology, which we’ve described and linked to on this site.
*Despite the complete lack of involvement of former staff in our decisionmaking, in general I favor more transparency in EA so ill note you are correct that RP has done research on lead exposure before, and in fact, two former RP staff now work on the Lead Exposure Action Fund. I think our ability to model lead was improved because we’d done work on it and this was a pro for us including it, as we felt more confident we could accurately do so by the time of launch. I was also a fund manager for the EA Animal Welfare Fund. We also have former staff at Longview, and some at other parts of Coefficient Giving unrelated to LEAF. We also have former staff or board members at a number of the funds we list that we plan to consider including Astralis, the AI Safety Tactical Opportunities Fund, Giving Green, and Macroscopic Ventures.
Thanks Marcus for the reply. First this criticism is specifically about potential preferences and bias of the researchers. With a project like this with perhaps hundreds of junctures which require subjective decisions, I think that’s a reasonable discussion to have. I don’t think it’s fair to ask me shift the ground and ask to discuss the research methodology, I’m sure there’ll be plenty of great discussion about that. My point was purely concerns about potential researcher bias.
I completely agree with your 3 points, and specifically that there are larger areas of concern than the cause area orientation of researchers—but I still think it’s somewhat important .
This comment seems to twist the point I was trying to make “More broadly, I would not consider Claude’s opinions about the priorities of our researchers to be a good method of gauging the quality of our work” I was not questioning the quality of your work, I think your work is extremely high quality. Yet even the highest quality work can go in different directions, based on the assumptions and biases of the researchers who generate it. In many imprecise fields like development, economics and political science, the highest quality researchers can argue opposite sides. Stiglitz and Friedman both produced high quality work which often ended up at almost opposite conclusions. I would put cross-cause categorisation in a similar-ish category due to the wealth of assumptions required and uncertainties to be reckoned with. So in research like these yes, I think the make-up of the research team is important and open to scrutiny outside of objective criticism of the work itself. You might disagree with me on this one. Assuming people’s backgrounds is open for discussion, I think a Claude analysis of people’s previous work is reasonable if obviously very imprecise and yes potentially misleading.
I could be wrong here, but I feel like I may have been baited-and-switched a little on who are the major contributors here.. I went to your website and just searched the cross-cause prioritisation team, finding a page with photos and bios. You’ve said that the researchers I listed were not the principal researchers - then who were? That Carmen van Schoubroeck led the work isn’t listed anywhere I don’t think.
I agree with the direction but not the strength of this comment “I can never tell you with certainty no one who worked on this was not subtly unconsciously biased” and agree with your opening statement more “I think there is not a single EA organization I would consider unbiased on this question, including ourselves” We are all biased, often more than we think. Most of us (myself included) have a cess-pit of opinions and angles, despite our best efforts to the contrary. I think within EA we often overrate how objective we are, and we can only have so much objectivity based on our past experience and worldview built up over time. This is why I think for a cross-cause prioritisation exercise, it is helpful to start with a team with a wide range of prior opinions or perhaps very uncertain ones.
Hi Nick—just regarding the team page issue, are you thinking of this page: https://rethinkpriorities.org/our-research-areas/worldview-investigations/ ? It has the people listed in your screenshot from Claude.
As a reference, I got to this from CCF page --> support our work --> under the “our team” section. Notably, the link is from this text:
“The Rethink Priorities Cross-Cause Fund sits on top of work done by our Worldview Investigations Team (WIT), and Interdisciplinary Research Team, groups established specifically to tackle the hard questions that most donors don’t have time to engage with: how to compare welfare across species, how to reason under deep uncertainty, how to weigh present benefits against future ones, how to aggregate competing moral views into a single allocation.
The team brings together training in philosophy, economics, statistics, cognitive science, moral psychology, and decision theory . The fund’s allocations also draw on the in-house expertise of RP’s Global Health and Development, Animal Welfare, AI departments, so the cross-cause model is informed by researchers working directly in each area, not just secondary literature. ”
So I’m interpreting that paragraph as saying that WIT work goes into the report, but not necessarily that the WIT team did all the work (in particular, the interdisciplinary research team clearly was also involved, and the second paragraph suggests other teams contributed as well).
Thanks so much @mal_graham🔸 that’s the web page appreciate that! I understand that the WIT doesn’t do all the work, but I think it was reasonable for me to assume that they were the major contributors given that they have been the team publishing the cross-cause work up to now, and were the team linked from the page.
Hey Nick, I don’t know if we are that far apart on the conceptual issue of potential bias but I think we are approaching this differently.
However, I really like the Stieglitz and Friedman analogy and think it is useful in many ways. Simply, there are lots of decision points here. And I really wish there were some other groups working on building such work so we could compare and contrast our work with theirs like you can in that context. If that were so, I think this type of meta-debate would be either less likely to occur, or more productive because we could point to more specific things.
At the same time, I think “count up the cause area backgrounds of the staff who worked on this” is misleading even if I agreed with the characterization of our staff, and I don’t. This is for a number of reasons and, if you’d permit, I’ll largely extend the economics and social science analogy to raise my points.
First, in some simple sense, I think looking at our staff’s background like this is like looking at the subfields GiveWell’s staff worked in prior to joining and if a disproportionate number worked on malaria using that to argue this is evidence of potential bias for why multiple of their top donation opportunities overall involve malaria. It’s not that this bias is not possible, it’s just a very blunt guide, at best, and the causal arrow may point the other way. That is, GiveWell may employ a lot of people who have backgrounds in malaria to help create their final estimates because they are particularly well suited to the task and/or malaria is an important area they have to cover.
Second, for this model many of the relevant choice points come from fields that have less ideological valence on the cause area lines (i.e. what do you think of risk attitudes) and potential bias in those areas is likely just as relevant to reaching conclusions as thinking at the level of cause area. Further, some inputs have effects difficult to pin down in the overall model, which limits the ability of people to even implicitly put their thumb on the scale because they don’t know what changing that input would change in the result. There are a few areas where it is obvious (i.e. make the animal moral weights higher or lower, reduce the cost-effectiveness of a given area) but those are the areas precisely where anyone looking at our model can see what we choose and object if they disagree.
But suppose you were convinced that Stiglitz tends to be biased in a liberal direction, and Friedman in a conservative one. What’s the best way to demonstrate that? I think it would often be to point to a specific assumption or choice they made that is questionable. This is why I asked you to point to something specific that you think is wrong. Not because I think it can’t be the case that we’re biased or that it’s inherently illegitimate to bring up the possibility, but because it’s the specifics that demonstrate that we are biased. I really do agree that too often some people in EA think everything they do is objective. I wrote this just last month and stand behind this as applying to basically everyone:
If someone doesn’t have the time to investigate an area, it can be at times reasonable to check for this kind of bias and discount appropriately (I think this is most useful as a guide when a group or individual has shown repeated bias in the past) but I also think this can at times serve as a shortcut to dismissing anything that doesn’t already ideologically align with yourself. Balancing these two competing forces can be a constant struggle, particularly in areas outside of your expertise and I know it’s something constantly in my mind when I read about politics.
Finally, to continue the analogy with economics, often the field of experts are debating a much more narrow range of opinions than exists in the public debate. For example, in immigration the economist debate about the impact of immigration on wages and employment is far more narrow than the general public discussion of the same topic. Stiglitz and Friedman (assuming you mean Milton Friedman) might have disagreed a lot but it was often within a largely shared framework. I’m pretty sure neither of them thought mass central planning would work, or that tariffs were largely paid by the exporting countries. I think many of the opinions people give about cause prioritization may fall into this category of something very few who think about the topic carefully dare to defend but, because those outside haven’t put in the time, they are unaware of this (in some sense through no fault of their own, not everyone can be an expert or very informed about every topic).
I think this type of distinction is likely to hold among serious efforts to build cross cause models.
Thanks there’s some insightful stuff here. I really appreciated this. “If someone doesn’t have the time to investigate an area, it can be at times reasonable to check for this kind of bias and discount appropriately (I think this is most useful as a guide when a group or individual has shown repeated bias in the past) but I also think this can at times serve as a shortcut to dismissing anything that doesn’t already ideologically align with yourself. Balancing these two competing forces can be a constant struggle, particularly in areas outside of your expertise and I know it’s something constantly in my mind when I read about politics.”
Despite my best efforts, I’ve really struggled to understand arguments around the nature of consciousness and valenced states. I’m still not entirely sure that any consciousness model is very meaningful but that’s another discussion… This means my analysis has often stepped up the level to process. As a side note I still think far more EA funds should go to animal welfare than currently does, although the defensive nature of responses from RP and other animal welfare folk has updated me a bit against this over the last couple of years.
I think the GiveWell analogy is a great one, and it would be fair to look at the backgrounds of staff that were doing prioritisatoin. If most came from malaria backgrounds I would be quite concerned, I haven’t looked into that. In a cross-cause prioritisation process I think there is more room for bias than in a CEA.
I think we’ll probably have to agree to disagree on research team make-up being open to outside scrutiny. Like you say I don’t think it’s the most important thing but still important,. You’ve said that “personel is policy” carries weight, but haven’t suggested how we should approach examining that? My Claude ranking method isn’t the best for sure, what other way you would suggest? From an RP organisational perspective, I think there is a risk that the org’s work could be undermined somewhat by people who might feel like a bunch of animal welfare/GCR folks might have disproportionate sway over a cross-cause prioritisation process like this.
I agree it would be great to have other groups working on this stuff, a poll a while ago was overwhelmingly in favour. The reality is though, you guys are it for now. There aren’t other groups working on moral weights and cross-cause prioritisation. I think this means that there’s perhaps more responsibility for balance within the organisation. The situation is more “Aristotle” dominating philosophy than Friedman and Stiglitz. I think with our current levels of information, thre is far more shared ground in economics than in cross-cause prioritisatoin. Perhaps within RP there’s much agreement but I would argue there will be a heavy “groupthink” element built there over time. That there are 10+ moral theories in this model illustrates the extreme diversity. Between animal welfare and human welfare we have a bridge of one moral weights project? While GCR uncertainties are far bigger still, with moral and practical junctures going almost uncountable. On the GCR front even something as important and straightforward as as “have those who worked on AI risk done ill or good so far” is hotly debated.
As a final (if a bit sour) note, RP as I’ve experienced it on the forum has seemed pretty impervious to criticism and suggestion. Responses are always intelligent and very well reasoned, but l haven’t seen openness to mind-changing, and not just in response to my comments. When criticism comes in, the response is polite well reasoned refutation—very good arguments for maintaining status-quo. I don’t think I’ve seen a response along the lines of “hey you have a good point there, let’s look into that” or even “yes that decision was tricky and we did X because...”. As an example I put a big effort into understanding the moral weights project (for personal interest reasons). Then after writing a decent post about the MWP process, the response from RP members was excellent and well thought through, but never acknowledged that any of my process points might be reasonable. In contrast orgs like GiveWell in my experience are far more open to mind changing.
Hey Nick,
I’m headed off to two weeks of conferencing in in a few hours so very likely won’t respond further after this but I do want to say a few things.
I take very seriously that you (or anyone else) believes we’ve come off as defensive or not open to changing our mind. I definitely think we haven’t always lived up to our communication ideals and could stand do better. I’m really sorry if you don’t think we aren’t open-minded but I don’t think this is true:
I don’t have the time to do a throughout search and systematic weighing but I believe this comment section contains at least one example to the contrary. I think just a few comments down someone raises questions about what went into the AI discount and I stated why it’s shaky, what changing it does to the output, and how I hope to see it improve.
I’m not really sure what the best method is as a I said I think doing this type of counting is a very blunt, limited tool. From my experience, the worry about our staff and outcome bias basically cuts all directions as some GCR people consider us too animal friendly, some animal people consider us too GCR and GHD friendly, and some GHD people (like yourself) consider us too animal and GCR friendly. I have personally heard all of these concerns over the past several months while working on this project. This doesn’t invalidate any of the concerns (we really could be biased one of these ways, not all concerns are equally valid) but I think it can bring some perspective to it. I think one inherent limitation to this enterprise is we typically get staff who are willing to work on multiple areas and often have explicitly done so prior to joining RP. But that typically means few if any are perceived from the outside as a true “member” of any cause area.
But I think an additional reason it’s not super useful to assess our staff this way and why I still lean away from the utility of type of exercise, is the actual projects RP does are reflective most of all of what projects we think are valuable. WIT in particular, has done roughly four large projects and those projects were about moral weight, risk and uncertainty in giving, quantitative tools for cross cause prioritization, and digital minds. But the reason we selected those projects—which I believe are then counted as part of what cause areas they’ve focused on in the Claude search—is because we thought there was a useful contribution to make that we could do well. To put it mildly, I think there’s far less big intellectual gaps in the GHD space than in AW and GCR space, or in cause prioritization itself. So, that we filled in gaps outside of GHD that we think are needed to improve cause prioritization may be being used as evidence we don’t care about GHD.
Outside of general competence, here’s what I actually prioritize on staff working on cause prio projects (not necessarily in this order):
Willingness and ability to be truthful to the arguments and the facts
Willingness to seek out and engage alternative views, and get expert feedback
Philosophical/empirical knowledge and breadth of experience working on the specific types of problems at hand
Of these I think (3) is partially visible from the outside, and under certain circumstances (2). (1) can be seen in what choices and arguments are actually advanced. But none of this maps very well onto “cause area” background, and largely I think different types of methodological background and experience have more to do with the output that area inclination. (I also don’t think cause areas are the relevant level of crux, cause areas contain bundles of interventions that are extremely heterogenous but that’s a story for another day).
I actually want more. I think we didn’t capture everything I think is relevant about moral theories and hope to do better on this in the future. We had to start somewhere though.
I get that we are largely it for now and take this responsibility very seriously. Suffice it to say this project has dominated my thinking for months, and my desire to do the best we can with our initial offering has been my primary concern for several months. Hopefully despite your concerns, future editions of this project and others can do more to win your trust and live up to what we need to be be if we’re going to be the only game in cause prioritization town.
Thanks that’s a fantastic reply, really appreciate all the humility thought, time and effort put in. Looking forward to seeing future work :)
Hi Marcus. Thanks for the clarifications.
I very much agree. I would be curious to know your thoughts on these specific critiques.
Hey Vasco, I replied to the last link here and I don’t have anything to add to Laura’s responses for your first two links.
In brief, the Donor Compass is streamlined, if you want more subtlety you probably should be using the advanced version of the tool. And I while I think we should aim to have secondary effects of interventions, I want to do it in a way that doesn’t unnecessarily penalize/reward areas where that data is/is not available, and to not have the effects of all interventions be dominated by deeply uncertain secondary impacts.
I replied there.
Great to know. Do you have any concrete plans or timelines? If not, how much funding would you need?
Nitpick. I would not call effects on non-target individuals “secondary”. I think they may be much larger than those on the target individuals (in expectation), and “secundary” makes it sound like they are less important to consider.
Makes sense.
What if, given reasonable moral and empirical views, we should in fact be very uncertain about whether practically any intervention is better or worse than nothing accounting for effects on non-target individuals? I think this would be useful to know. The model should not force the conclusion that interventions which have historically been supported by the effective altruism community (like saving human lives, and cage-free egg campaigns) are better than nothing for all reasonable moral and empirical views?