I hear two conflicting voices in my head, and in EA:
- Voice: it's highly uncertain whether deworming is effective, based on 20 years of research, randomized controlled trials, and lots of feedback. In fact, many development interventions have a small or negative impact.
- Same voice: we are confident that work for improving the far future is effective, based on <insert argument involving the number of stars in the universe>.
I believe that I could become convinced to work on artificial intelligence or extinction risk reduction. My main crux is that these problems seem intractable. I am worried that my work would have a negligible or a negative impact.
These questions are not sufficiently addressed yet, in my opinion. So far, I've seen mainly vague recommendations (e.g., "community building work does not increase risks" or "look at the success of nuclear disarmament"). Examples of existing work for improving the far future often feel very indirect (e.g., "build a tool to better estimate probabilities ⇒ make better decisions ⇒ facilitate better coordination ⇒ reduce the likelihood of conflict ⇒ prevent a global war ⇒ avoid extinction") and thus disconnected from actual benefits for humanity.
One could argue that uncertainty is not a problem, that it is negligible when considering the huge potential benefit of work for the far future. Moreover, impact is fat-tailed, and thus the expected value dominated by a few really impactful projects, and thus it's worth trying projects even if they have low success probability. This makes sense, but only if we can protect against large negative impacts. I doubt we really can — for example, a case can be made that even safety-focused AI researchers accelerate AI and thus increase its risks.
One could argue that community building or writing "what we owe the future" are concrete ways to do good for the future . Yet this seems to shift the problem rather than solve it. Consider a community builder who convinces 100 people to work on improving the far future. There are now 100 people doing work with uncertain, possibly-negative impact. The community builder's impact is some function f(x1,...,x100) which is similarly uncertain and possibly negative. This is especially true if x is fat-tailed, as the impact will be dominated by the most successful (or most destructive) people.
To summarize: How can we reliably improve the far future, given that even near-termist work like deworming, with plenty of available data and research and rapid feedback loops and simple theories, so often fails? As someone who is eager to do spend my work time well, who thinks that our moral circle should include the future, but who does not know ways to reliably improve it... what should I do?
Some ideas for career paths that I think have a very low chance of terrible outcomes and a reasonable chance to do a ton of good for the long-term future (I'm not claiming that they definitely will be net-positive, I'm claiming they are more than 10x more likely to be net positive than to be net negative):
Besides these, I think that almost all work longtermists work on today has a positive expected value, even if it has large downsides. Your comparison to deworming isn't perfect. Failed deworming is not causing direct harm. It is still better to give money to ineffective deworming than to do nothing.
This is valuable, thank you. I really like the point on early warning systems for pandemics.
Regarding the bioweapons convention, I intuitively agree. I do have some concerns about how it could tip power balances (akin to how abortion bans tend to increase illegal abortions and put women at risk, but that's a weak analogy). There is also a historical example of how the Geneva Disarmament Conference inspired Japan's bioweapons program.
Predicting how fast powerful AI is going to be developed: That one seems value-neutral to me. It could help regular AI as much as AI safety. Why do you think it's 10x more likely to be beneficial?
AI alignment research and AI governance: I would like to agree with you, and part of me does... I've outlined my hesitations in the comment below.
Re: bioweapons convention: Good point, so maybe not as straightforward as I described.
Re: predicting AI: You can always not publish the research you are doing or only inform safety-focused institutions about it. I agree that there are some possible downsides to knowing more precisely when AI will be developed, but there seem to be much worse downsides to not knowing when AI will be developed (mainly that nobody is preparing for it policy- and coordination-wise)
I think the biggest risk is getting governments too excited about AI. So I'm actually not super confident that any work on this is 10x more likely to be positive.
Re: policy & alignment: I'm very confident, that there is some form of alignment work that is not speeding up capabilities, especially the more abstract one. Though I agree on interpretability. On policy, I would also be surprised if every avenue of governance was as risky as you describe. Especially laying out big picture strategies and monitoring AI development seem pretty low-risk.
Overall, I think you have done a good job scrutinizing my claims and I'm much less confident now. Still, I'd be really surprised if every type of longtermist work was as risky as your examples - especially for someone as safety-conscious as you are. (Actually, one very positive thing might be criticizing different approaches and showing their downsides)
Thanks a lot for your responses!
I share your sentiment: there must be some form of alignment work that is not speeding up capabilities, some form of longtermist work that isn't risky... right?
Why are the examples so elusive? I think this is the core of the present forum post.
15 years ago, when GiveWell started, the search for good interventions was difficult. It required a lot of research, trials, reasoning etc. to find the current recommendations. We are at a similar point for work targeting the far future... except that we can't do experiments, don't have feedback, don't have historical examples[1], etc. This makes the question a much harder one. It also means that "do research on good interventions" isn't a good answer either, since this research is so intractable.
Ian Morris in this podcast episode discusses to what degree history is contingent, i.e., past events have influenced the future for a long time. ↩︎
Apologies in advance for being nitpicky. But you could consider the counterfactual where the money would instead go to another effective charity. A similar point holds for AI safety outreach: it may cause people to switch careers and move away from other promising areas, or cause people to stop earning to give.
Sorry if your bar for "reliable good" entails being clearly better than counterfactuals with high confidence, then afaict literally nothing in EA clears that bar. Certainly none of the other Givewell charities clear this bar.
I don't mean to set an unreasonably high bar. Sorry if my comment came across that way.
It's important to use the right counterfactual because work for the long-term future competes with GiveWell-style charities. This is clearly the message of 80000hours.org, for example. After all, we want to do the most good we can, and it's not enough to do better than zero.
I'm probably confused about what you're saying, but how is this different from saying that work on Givewell-style charities compete with the long-term future, and also donations to Givewell-style charities compete with each other?