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.
Note that we didn’t tell them the topic that specifically.
I understand that, and think this was the right call. But there seems to be consensusthat in general, a response rate below ~70% introduces concerns of non-response bias, and when you’re at 15%—with (imo) good reason to think there would be non-response bias—you really cannot rule this out. (Even basic stuff like: responders probably earn less money than non-responders, and are thus probably younger, work in academia rather than industry, etc.; responders are more likely to be familiar with the prior AI Impacts survey, and all that that entails; and so on.) In short, there is a reason many medical journals have a policy of not publishing surveys with response rates below 60%; e.g., JAMA asks for >60%, less prestigious JAMA journals also ask for >60%, and BMJ asks for >65%. (I cite medical journals because their policies are the ones I’m most familiar with, not because I think there’s something special about medical journals.)
Tried sending them $100 last year and if anything it lowered the response rate.
I find it a bit hard to believe that this lowered response rates (was this statistically significant?), although I would buy that it didn’t increase response rates much, since I think I remember reading that response rates fall off pretty quickly as compensation for survey respondents increases. I also appreciate that you’re studying a high-earning group of experts, making it difficult to incentivize participation. That said, my reaction to this is: determine what the higher-order goals of this kind of project are, and adopt a methodology that aligns with that. I have a hard time believing that at this price point, conducting a survey with a 15% response rate is the optimal methodology.
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.
My impression stems from conversations I’ve had with two CS professor friends about how concerned the CS community is about the risks posed by AI. For instance, last week, I was discussing the last AI Impacts survey with a CS professor (who has conducted surveys, as have I); I was defending the survey, and they were criticizing it for reasons similar to those outlined above. They said something to the effect of: the AI Impacts survey results do not align with my impression of people’s level of concern based on discussions I’ve had with friends and colleagues in the field. And I took that seriously, because this friend is EA-adjacent; extremely competent, careful, and trustworthy; and themselves sympathetic to concerns about AI risk. (I recognize I’m not giving you enough information for this to be at all worth updating on for you, but I’m just trying to give some context for my own skepticism, since you asked.)
Lastly, as someone immersed in the EA community myself, I think my bias is—if anything—in the direction of wanting to believe these results, but I just don’t think I should update much based on a survey with such a low response rate.
I think this is going to be my last word on the issue, since I suspect we’d need to delve more deeply into the literature on non-response bias/response rates to progress this discussion, and I don’t really have time to do that, but if you/others want to, I would definitely be eager to learn more.
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.
I understand that, and think this was the right call. But there seems to be consensus that in general, a response rate below ~70% introduces concerns of non-response bias, and when you’re at 15%—with (imo) good reason to think there would be non-response bias—you really cannot rule this out. (Even basic stuff like: responders probably earn less money than non-responders, and are thus probably younger, work in academia rather than industry, etc.; responders are more likely to be familiar with the prior AI Impacts survey, and all that that entails; and so on.) In short, there is a reason many medical journals have a policy of not publishing surveys with response rates below 60%; e.g., JAMA asks for >60%, less prestigious JAMA journals also ask for >60%, and BMJ asks for >65%. (I cite medical journals because their policies are the ones I’m most familiar with, not because I think there’s something special about medical journals.)
I find it a bit hard to believe that this lowered response rates (was this statistically significant?), although I would buy that it didn’t increase response rates much, since I think I remember reading that response rates fall off pretty quickly as compensation for survey respondents increases. I also appreciate that you’re studying a high-earning group of experts, making it difficult to incentivize participation. That said, my reaction to this is: determine what the higher-order goals of this kind of project are, and adopt a methodology that aligns with that. I have a hard time believing that at this price point, conducting a survey with a 15% response rate is the optimal methodology.
My impression stems from conversations I’ve had with two CS professor friends about how concerned the CS community is about the risks posed by AI. For instance, last week, I was discussing the last AI Impacts survey with a CS professor (who has conducted surveys, as have I); I was defending the survey, and they were criticizing it for reasons similar to those outlined above. They said something to the effect of: the AI Impacts survey results do not align with my impression of people’s level of concern based on discussions I’ve had with friends and colleagues in the field. And I took that seriously, because this friend is EA-adjacent; extremely competent, careful, and trustworthy; and themselves sympathetic to concerns about AI risk. (I recognize I’m not giving you enough information for this to be at all worth updating on for you, but I’m just trying to give some context for my own skepticism, since you asked.)
Lastly, as someone immersed in the EA community myself, I think my bias is—if anything—in the direction of wanting to believe these results, but I just don’t think I should update much based on a survey with such a low response rate.
I think this is going to be my last word on the issue, since I suspect we’d need to delve more deeply into the literature on non-response bias/response rates to progress this discussion, and I don’t really have time to do that, but if you/others want to, I would definitely be eager to learn more.