Hide table of contents

A few months ago I was with several members of the effective altruism community on a video chat about how to compare different effective altruism groups. Someone recommended that we try to have a meta-evaluation to rank them, but on some further discussion it became apparent that that would be incredibly difficult to do.

The different organizations just don’t have the same goals as each other. Many were “effective” within their own standard of epistemology, but not in those of the others. So comparing them was more about comparing general epistemologies then doing direct comparisons of the organizations themselves.

Here is my current attempt at making some sense of the “main” effective altruist groups, based on their disagreements on epistemology.

Charity evaluators

It makes sense to begin with Givewell, because they seem to be the first and most recognized group within Effective Altruism. Upon launch in 2007 they promoted the idea that charities could be evaluated on the efficiency of money put in to the amount of “good” outputted. This was backed up with research by the GiveWell team to identify and recognize the “most efficient” charities. This made a lot of sense to many smart people, but soon things became quite complicated as groups also trying to “do the most good” within charity evaluation emerged to somewhat compete with them.

GiveWell is reluctant to investigate animal welfare charities, seemingly because its founders value animals substantially less than humans. Other people have decided that animals are worth more in respect to humans than Givewell has indicated, and some of these have set up a “GiveWell for Animals”, or Effective Animal Altruism. Aidgrade was established a semi-Givewell competitor and disagrees with them about the proper methods to do charity evaluations.

Effective altruism funders

Then come the more somewhat high-risk meta-charities that promote giving to Effective Altruist groups, like Giving What We Can, The Life You Can Save, and 80,000 Hours. These groups are really interesting to me because on paper what they’re doing seems to be working (Giving What We Can has estimated to be able to encourage the giving of around $8 for every $1 donated to them, for instance).

Yet from what I understand, GiveWell refuses to recommend any of these as top charities. My impression is that GiveWell finds it highly unlikely that any of these organizations are as effective as their recommended charities. Of course, many of these organizations exist on the assumption that they are. This area seems particularly awkward as all of these meta-charities promote GiveWell publicly, leading to several interviews. I imagine that it’s better off for everyone that Givewell and CEA appear as close friends, yet internally it seems like there’s a bit of tension over this stark disagreement on the need for CEA’s existence. This disagreement is somewhat showcased in the comments here.

Also of note here is Effective Fundraising. This group is directly writing grants for both the AMF (GiveWell’s top choice) and the Humane League (Effective Animal Activism’s top choice). They’ve discussed why they split between them here. 80,000 Hours and CEA have interviewed them and written a blog post with a very nice name and slightly less praising contents. There’s a bit of back and forth between them in the comments, which I referred to earlier.

AI risk and far future researchers

Going further, when coming to the topic of AI risk, things get much more confusing. Organizations like MIRI and FHI claim that AI risk and other existential risks are by far the most important causes. Holden of Givewell wrote a very long critique here, which there’s since been quite a bit of debate about. I suspect that incredibly few people actually have a deep understanding of the efficiencies or even importance of donating to prevent AI risk, and it is quite apparent that most donating will have to go with either a gut feeling or choose based on which social group they trust more.

Brian Tomasik has been a very interesting and rather independent thinker within the movement, known for his series of Utilitarian Essays. Even more than animal causes, he has numbers to recommend donating for insect causes that make it seem like a fantastic area for efficient intervention. The main argument I’ve heard from others against insect causes is that they seem intuitively silly, not that they aren’t actually effective.

Recently Brian has made his own research institute called FRI to focus on long term suffering reduction. Sure enough it’s his number 1 recommended charity, but more surprisingly to me is that the number 2 isn’t another similar far-future venture like MIRI or FHI, but instead Animal Ethics. I suppose he’s both very concerned with the far future, but not very concerned with AI risk (the main reason he wouldn’t recommend MIRI or FHI).

Another organization that comes to my mind is Leverage Research. I’m not sure what category to put them in because they seem to be trying to do a bit of everything. However, I would point out that they appear to be the polar opposites of GiveWell regarding to belief in transparency, with all of the other organizations mentioned above are somewhat in between (with the possible exception of Effective Fundraising). My belief is that their expected method of doing the most good involves having a few smart people spend several years thinking about “big issues”, without much care for outsider input (you’ll notice a lack of much information on their website). These research topics seem to be more psychological and social than AI-risk, from what I understand. Most other EA organizations seem very skeptical of their research posted so far, mainly Connection Theory. However, a few people within Leverage run Think!, and they have created the Effective Altruist Summit, which has gotten good reviews.

Future organizations

I really hope that the issue of correct epistemology gets resolved and more of these organizations can at least agree on some standard ways of Effective Altruist Organization evaluation. But it seems unlikely to happen anytime soon.

The current trend from my point of view seems to be that every other new prominent Effective Altruist thinker will create a new organization to match another unique epistemology. I really hope that we don’t get stuck with 20 different Charity Evaluators of slightly differing standards and 50 funding groups that randomly give to selections from those different evaluators, and another 10 Far Future groups that will emphasize slightly different things because of unique epistemological reasons. But given the fact that there is so much disagreement within what seems like such a small and intelligent group already, I could definitely see things going this way.

Granted, this still is a lot better than what is the global cultural norm right now. I would expect that most of the groups above average are far more useful than the global human average. But the global norm is pathetically low, so that shouldn’t be considered too important. Within these groups, I’d expect that many consider order of magnitude differences between them, so internal fragmentation may cause quite a bit of damage. And it’s definitely confusing as anything to a newcomer who just wants to be an “Efficient Altruist”.

Personal bias

I should point out that I know people from most of the organizations mentioned, so probably am quite biased. I am presently working at 80,000 Hours, which is based in the same office as Giving What we Can, The Center For Effective Altruism, and The Future of Humanity Institute. Recently I applied to and was rejected from working at Leverage Research.

Blog efficiency

This post took me about 5 hours to write. I ended up splitting my thoughts into two posts (leaving the rest for one more to come shortly). I’ve tracked 4 Pomodoros in this time, but then I just kept on working for a few hours without tracking. I originally intended for this to be about 2 Pomodoros, but this was for a small comment about “Epistemology as a Deus Ex Machina” rather than a small EA Org overview.

My guess is that I’ll dedicate at least another hour or two to checking stats of readership for this post and/or reading/responding to comments.

Crossposted from Ozzie Gooen's blog

Comments1


Sorted by Click to highlight new comments since:

"Yet from what I understand, GiveWell refuses to recommend any of these as top charities. My impression is that GiveWell finds it highly unlikely that any of these organizations are as effective as their recommended charities. Of course, many of these organizations exist on the assumption that they are. This area seems particularly awkward as all of these meta-charities promote GiveWell publicly, leading to several interviews. I imagine that it’s better off for everyone that Givewell and CEA appear as close friends, yet internally it seems like there’s a bit of tension over this stark disagreement on the need for CEA’s existence. This disagreement is somewhat showcased in the comments here."

Regardless of whether GiveWell thought that CEA's organisation were more effective than their own recommendations, I think it is rational for GiveWell not to recommend CEA's organisations. Such a recommendation would quickly lead to the 'infinte regression problem' (one should donate to an organisation, that encourages people to donate to an organisation, that encourages people to donate to an organisation, that... etc. ... that encourages people to donation to effective first order work. See Ben Todd's Master's thesis on career choice for more discussion). GiveWell would risk the accusation of contributing to some sort of charitable ponzi scheme, which is an accusation that I have heard made when a charity evaluator has discussed recommending another charity evaluator. Of course there are ways around this in practise (again see Ben Todd's thesis), but it would still pose a reputational risk for GiveWell to recommend a CEA organisation given their status as meta-charities.

Curated and popular this week
LintzA
 ·  · 15m read
 · 
Cross-posted to Lesswrong Introduction Several developments over the past few months should cause you to re-evaluate what you are doing. These include: 1. Updates toward short timelines 2. The Trump presidency 3. The o1 (inference-time compute scaling) paradigm 4. Deepseek 5. Stargate/AI datacenter spending 6. Increased internal deployment 7. Absence of AI x-risk/safety considerations in mainstream AI discourse Taken together, these are enough to render many existing AI governance strategies obsolete (and probably some technical safety strategies too). There's a good chance we're entering crunch time and that should absolutely affect your theory of change and what you plan to work on. In this piece I try to give a quick summary of these developments and think through the broader implications these have for AI safety. At the end of the piece I give some quick initial thoughts on how these developments affect what safety-concerned folks should be prioritizing. These are early days and I expect many of my takes will shift, look forward to discussing in the comments!  Implications of recent developments Updates toward short timelines There’s general agreement that timelines are likely to be far shorter than most expected. Both Sam Altman and Dario Amodei have recently said they expect AGI within the next 3 years. Anecdotally, nearly everyone I know or have heard of who was expecting longer timelines has updated significantly toward short timelines (<5 years). E.g. Ajeya’s median estimate is that 99% of fully-remote jobs will be automatable in roughly 6-8 years, 5+ years earlier than her 2023 estimate. On a quick look, prediction markets seem to have shifted to short timelines (e.g. Metaculus[1] & Manifold appear to have roughly 2030 median timelines to AGI, though haven’t moved dramatically in recent months). We’ve consistently seen performance on benchmarks far exceed what most predicted. Most recently, Epoch was surprised to see OpenAI’s o3 model achi
Dr Kassim
 ·  · 4m read
 · 
Hey everyone, I’ve been going through the EA Introductory Program, and I have to admit some of these ideas make sense, but others leave me with more questions than answers. I’m trying to wrap my head around certain core EA principles, and the more I think about them, the more I wonder: Am I misunderstanding, or are there blind spots in EA’s approach? I’d really love to hear what others think. Maybe you can help me clarify some of my doubts. Or maybe you share the same reservations? Let’s talk. Cause Prioritization. Does It Ignore Political and Social Reality? EA focuses on doing the most good per dollar, which makes sense in theory. But does it hold up when you apply it to real world contexts especially in countries like Uganda? Take malaria prevention. It’s a top EA cause because it’s highly cost effective $5,000 can save a life through bed nets (GiveWell, 2023). But what happens when government corruption or instability disrupts these programs? The Global Fund scandal in Uganda saw $1.6 million in malaria aid mismanaged (Global Fund Audit Report, 2016). If money isn’t reaching the people it’s meant to help, is it really the best use of resources? And what about leadership changes? Policies shift unpredictably here. A national animal welfare initiative I supported lost momentum when political priorities changed. How does EA factor in these uncertainties when prioritizing causes? It feels like EA assumes a stable world where money always achieves the intended impact. But what if that’s not the world we live in? Long termism. A Luxury When the Present Is in Crisis? I get why long termists argue that future people matter. But should we really prioritize them over people suffering today? Long termism tells us that existential risks like AI could wipe out trillions of future lives. But in Uganda, we’re losing lives now—1,500+ die from rabies annually (WHO, 2021), and 41% of children suffer from stunting due to malnutrition (UNICEF, 2022). These are preventable d
Rory Fenton
 ·  · 6m read
 · 
Cross-posted from my blog. Contrary to my carefully crafted brand as a weak nerd, I go to a local CrossFit gym a few times a week. Every year, the gym raises funds for a scholarship for teens from lower-income families to attend their summer camp program. I don’t know how many Crossfit-interested low-income teens there are in my small town, but I’ll guess there are perhaps 2 of them who would benefit from the scholarship. After all, CrossFit is pretty niche, and the town is small. Helping youngsters get swole in the Pacific Northwest is not exactly as cost-effective as preventing malaria in Malawi. But I notice I feel drawn to supporting the scholarship anyway. Every time it pops in my head I think, “My money could fully solve this problem”. The camp only costs a few hundred dollars per kid and if there are just 2 kids who need support, I could give $500 and there would no longer be teenagers in my town who want to go to a CrossFit summer camp but can’t. Thanks to me, the hero, this problem would be entirely solved. 100%. That is not how most nonprofit work feels to me. You are only ever making small dents in important problems I want to work on big problems. Global poverty. Malaria. Everyone not suddenly dying. But if I’m honest, what I really want is to solve those problems. Me, personally, solve them. This is a continued source of frustration and sadness because I absolutely cannot solve those problems. Consider what else my $500 CrossFit scholarship might do: * I want to save lives, and USAID suddenly stops giving $7 billion a year to PEPFAR. So I give $500 to the Rapid Response Fund. My donation solves 0.000001% of the problem and I feel like I have failed. * I want to solve climate change, and getting to net zero will require stopping or removing emissions of 1,500 billion tons of carbon dioxide. I give $500 to a policy nonprofit that reduces emissions, in expectation, by 50 tons. My donation solves 0.000000003% of the problem and I feel like I have f