Hi @Will Aldred, thank you for the question! It’s one we’ve also thought about. Here are three additional considerations that may help explain this result.
The sample size in this case study was very small. Only 4-6 experts forecasted on each node, so we want to be wary of reading too much into any particular number. As people continue to forecast on the Metaculus and Manifold versions of this question we should get more data about this one!
The warning shot question here was specifically about administrative disempowerment (as opposed to deaths, property damage, or anything else). It’s possible that experts think this type of warning shot wouldn’t prompt the type of reaction that would end up reducing x-risk, or that it would be particularly hard to recover from.
In our adversarial collaboration on AI risk, we asked about “escalating warning shots,” where the warning shots involve killing large numbers of people or causing large amounts of damage. The “concerned” participants in that study had substantial overlap with the experts in this one. Conditional on that question resolving positively, the concerned group would have a lower P(doom), mostly for the reasons you said. This could be because of the different operationalization, or just because the small number of people who were randomly assigned to forecast on the administrative disempowerment warning shot question don’t expect society to respond well to warning shots.
We are very interested in what types of concerning AI behavior would be most likely to influence different responses from policymakers, and we’re hoping to continue to work on related questions in future projects!
Hi @Will Aldred, thank you for the question! It’s one we’ve also thought about. Here are three additional considerations that may help explain this result.
The sample size in this case study was very small. Only 4-6 experts forecasted on each node, so we want to be wary of reading too much into any particular number. As people continue to forecast on the Metaculus and Manifold versions of this question we should get more data about this one!
The warning shot question here was specifically about administrative disempowerment (as opposed to deaths, property damage, or anything else). It’s possible that experts think this type of warning shot wouldn’t prompt the type of reaction that would end up reducing x-risk, or that it would be particularly hard to recover from.
In our adversarial collaboration on AI risk, we asked about “escalating warning shots,” where the warning shots involve killing large numbers of people or causing large amounts of damage. The “concerned” participants in that study had substantial overlap with the experts in this one. Conditional on that question resolving positively, the concerned group would have a lower P(doom), mostly for the reasons you said. This could be because of the different operationalization, or just because the small number of people who were randomly assigned to forecast on the administrative disempowerment warning shot question don’t expect society to respond well to warning shots.
We are very interested in what types of concerning AI behavior would be most likely to influence different responses from policymakers, and we’re hoping to continue to work on related questions in future projects!