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 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.