many AI researchers just don’t seem too concerned about the risks posed by AI, so may not have opened the survey
Note that we didn't tell them the topic that specifically.
I am wondering whether a better approach would instead be to randomly sample a subset of potential respondents (say, 4,000 people), and offer to compensate them at a much higher rate (e.g., $100)..
Tried sending them $100 last year and if anything it lowered the response rate.
If you are inclined to dismiss this based on your premise "many AI researchers just don’t seem too concerned about the risks posed by AI", I'm curious where you get that view from, and why you think it is a less biased source.
Thank you both for your past and future work on EV, and best wishes to both of you in your new roles. Really looking forward to seeing you more in the geographic vicinity of Open Phil!
Possible, but likely a smaller effect than you might think because:
a) I was very ambiguous about the subject matter until they were taking the survey (e.g. did not mention AGI or risk or timelines)
b) Last time (for the 2016 survey) we checked the demographics of respondents against those for a random subset of non-respondents, and they weren't very different.
Participants were also mostly offered substantial payment for taking the survey ($50 usually, for a ~15m survey), in part in the hope of making payment a larger motivator than desire to express some particular view, but I don't think payment actually made a large difference to the response rate, so probably failed have the desired effect on possible response bias.
>I would be very excited to see research by Giving Green into whether their approach of recommending charities which are, by their own analysis, much less cost effective than the best options is indeed justified.
Several confusions I have:
When did they say these were much less cost-effective? I thought they just failed to analyze cost effectiveness? (Which is also troubling, but different from what you are saying, so I'm confused)
What do you mean by it being justified? It looks like you mean 'does well on a comparison of immediate impact', but, supposing these things are likely to be interpreted as recommendations about what is most cost-effective, this approach sounds close to outright dishonesty, which seems like it would still not be justified. (I'm not sure to what extent they are presenting them that way.)
Do they explicitly say that this is their approach?
Do you have quantitative views on the effectiveness of donating these organizations, that could be compared to other actions? (Or could you point me to any of the links go to something like that?) Sorry if I missed them.
It seems worth distinguishing 'effectiveness' in the sense of personal competence (as I guess is meant in the first case, e.g. 'reasonably sharp') and 'effectiveness' in the sense of trying to choose interventions by cost-effectiveness.
Also remember that selecting people to encourage in particular directions is a subset of selecting interventions. It may be that 'E not A' people are more likely to be helpful than 'A not E' people, but that chasing either group is less helpful than doing research on E that is helpful for whichever people already care about it. I think I have stronger feelings about E-improving interventions overall being good than about which people are more promising allies.
Our 2024 survey results should be out soon.
Note that we didn't tell them the topic that specifically.
Tried sending them $100 last year and if anything it lowered the response rate.
If you are inclined to dismiss this based on your premise "many AI researchers just don’t seem too concerned about the risks posed by AI", I'm curious where you get that view from, and why you think it is a less biased source.
Thank you both for your past and future work on EV, and best wishes to both of you in your new roles. Really looking forward to seeing you more in the geographic vicinity of Open Phil!
Longer/ongoing list here.
Possible, but likely a smaller effect than you might think because: a) I was very ambiguous about the subject matter until they were taking the survey (e.g. did not mention AGI or risk or timelines) b) Last time (for the 2016 survey) we checked the demographics of respondents against those for a random subset of non-respondents, and they weren't very different.
Participants were also mostly offered substantial payment for taking the survey ($50 usually, for a ~15m survey), in part in the hope of making payment a larger motivator than desire to express some particular view, but I don't think payment actually made a large difference to the response rate, so probably failed have the desired effect on possible response bias.
I respond here: https://worldspiritsockpuppet.com/2021/03/09/why-does-ads-think-ea-hasnt-grown.html
>I would be very excited to see research by Giving Green into whether their approach of recommending charities which are, by their own analysis, much less cost effective than the best options is indeed justified.
Several confusions I have:
Do you have quantitative views on the effectiveness of donating these organizations, that could be compared to other actions? (Or could you point me to any of the links go to something like that?) Sorry if I missed them.
It seems worth distinguishing 'effectiveness' in the sense of personal competence (as I guess is meant in the first case, e.g. 'reasonably sharp') and 'effectiveness' in the sense of trying to choose interventions by cost-effectiveness.
Also remember that selecting people to encourage in particular directions is a subset of selecting interventions. It may be that 'E not A' people are more likely to be helpful than 'A not E' people, but that chasing either group is less helpful than doing research on E that is helpful for whichever people already care about it. I think I have stronger feelings about E-improving interventions overall being good than about which people are more promising allies.
Yeah, and among common intuitions I think. But I thought EAs were mostly consequentialists, so the intended role of obligations is not obvious to me.