The cause prioritization landscape in EA is changing.
- Focus has shifted away from evaluation of general cause areas or cross-cause comparisons, with the vast majority of research now comparing interventions within particular cause areas.
- Artificial Intelligence does not comfortably fit into any of the traditional cause buckets of Global Health, Animal Welfare, and Existential Risk. As EA becomes increasingly focused on AI, traditional cause comparisons may ignore important considerations.
- While some traditional cause prioritization cruxes remain central (e.g. animal vs. human moral weights, cluelessness about the longterm future), we expect new cruxes have emerged that are important for people’s giving decisions today but have received much less attention.
We want to get a better picture of what the most pressing cause prioritization questions are right now. This will help us, as a community, decide what research is most needed and open up new lines of inquiry. Some of these questions may be well known in EA but still unanswered. Some may be known elsewhere but neglected in EA. Some may be brand new. To elicit these cruxes, consider the following question:
Imagine that you are to receive $20 million at the beginning of 2026. You are committed to giving all of it away, but you don’t have to donate on any particular timeline. What are the most important questions that you would want answers to before deciding how, where, and when to give?
Great question, thank you for working on this. An inter-cause-prio-crux that I have been wondering about is something along the lines of:
"How likely is it that a world where AI goes well for humans also goes well for other sentient beings?"
It could probably be much more precise and nuanced, but specifically, I would want to assess whether "trying to make AI go well for all sentient beings" is marginally better supported through directly related work (e.g., AIxAnimals work) or through conventional AI safety measures - the latter of which would be supported if, e.g., making AI go well for humans will inevitably or is necessary to make sure that AI goes well for all. Although if it is necessary, it would depend further on how likely AI will go well for humans and such; but I think a general assessment of AI futures that go well for humans would be a great and useful starting point for me.
I also think various explicit estimates of how neglected exactly a (sub-)cause area is (e.g., in FTE or total funding) would greatly inform some inter-cause-prio questions I have been wondering about - assuming that explicit marginal cost-effectiveness estimates aren't really possible, this seems like the most common proxy I refer to that I am missing solid numbers on.