I don’t know if a rough analogy might help, but imagine you just bought a house . The realtor warns you that some houses in this neighbourhood have faulty wiring, and your house might randomly set on fire during the 5 years or so you plan to live in it (that is, there is a 10% or whatever chance per year the house sets on fire). There are certain precautions you might take, like investing in a fire blanket and making sure your emergency exits are always clear, but principally buying very good home insurance, at a very high premium.
Imagine then you meet a builder in a bar and he says, “Oh yes, Smith was a terrible electrician and any house Smith built has faulty wiring, giving it a 50% chance of fire each year. If Smith didn’t do your wiring then it is no more risky than any other house, maybe 1% per year”. You don’t actually live in a house with a 10% risk, you live in a house with a 1% or 50% risk. Each of those houses necessitates a different strategy—in a low risk house you can basically take no action, and save money on the premium insurance. In the high risk house you want to basically sell immediately (or replace the wiring completely). One important thing you would want to do straight away is discover if Smith or Jones built your house, which is irrelevant information in the first situation before you met the builder in the bar, where you implicitly have perfect certainty. You might reason inductively—“I saw a fire this year, so it is highly likely I live in a home that Smith built, so I am going to sell at a loss to avoid the fire which will inevitably happen next year” (compared to the first situation where you would just reason you were unlucky)
I totally agree with your final paragraph—to actually do anything with the information there is an asymmetrically distributed ex post AI Risk requires a totally different model. This is not an essay about what to actually do about AI Risk. However hopefully this comment gives perhaps a sketch picture of what might be accomplished when such a model is designed and deployed.
I’m not sure that this responds to the objection. Specifically, I think that we would need to clarify what is meant by ‘risk’ here. It sounds like what you’re imagining is having credences over objective chances. The typical case of that would be not knowing whether a coin was biased or not, where the biased one would have (say) 90% chance of heads, and having a credence about whether the coin is biased. In such a case the hypotheses would be chance-statements, and it does make sense to have credences over them.
However, it’s unclear to me whether we can view either the house example or AGI risk as involving objective chances. The most plausible interpretation of an objective chance usually involves a pretty clear stochastic causal mechanism (and some would limit real chances to quantum events). But if we don’t want to allow talk of objective chances, then all the evidence you receive about Smith’s electricity skills, and the probability that they built the house, is just more evidence to conditionalize your credences on, which will leave you with a new final credence over the proposition we ultimately care about: whether your house will burn down. If so, the levels wouldn’t make sense, I think, and you should just multiply through.
I’m not sure how this affects the overall method and argument, but I do wonder whether it would be helpful to be more explicit what is on the respective axes of the graphs (e.g. the first bar chart), and what exactly is meant by risk, to avoid risks of equivocation.
I don’t know if a rough analogy might help, but imagine you just bought a house . The realtor warns you that some houses in this neighbourhood have faulty wiring, and your house might randomly set on fire during the 5 years or so you plan to live in it (that is, there is a 10% or whatever chance per year the house sets on fire). There are certain precautions you might take, like investing in a fire blanket and making sure your emergency exits are always clear, but principally buying very good home insurance, at a very high premium.
Imagine then you meet a builder in a bar and he says, “Oh yes, Smith was a terrible electrician and any house Smith built has faulty wiring, giving it a 50% chance of fire each year. If Smith didn’t do your wiring then it is no more risky than any other house, maybe 1% per year”. You don’t actually live in a house with a 10% risk, you live in a house with a 1% or 50% risk. Each of those houses necessitates a different strategy—in a low risk house you can basically take no action, and save money on the premium insurance. In the high risk house you want to basically sell immediately (or replace the wiring completely). One important thing you would want to do straight away is discover if Smith or Jones built your house, which is irrelevant information in the first situation before you met the builder in the bar, where you implicitly have perfect certainty. You might reason inductively—“I saw a fire this year, so it is highly likely I live in a home that Smith built, so I am going to sell at a loss to avoid the fire which will inevitably happen next year” (compared to the first situation where you would just reason you were unlucky)
I totally agree with your final paragraph—to actually do anything with the information there is an asymmetrically distributed ex post AI Risk requires a totally different model. This is not an essay about what to actually do about AI Risk. However hopefully this comment gives perhaps a sketch picture of what might be accomplished when such a model is designed and deployed.
I’m not sure that this responds to the objection. Specifically, I think that we would need to clarify what is meant by ‘risk’ here. It sounds like what you’re imagining is having credences over objective chances. The typical case of that would be not knowing whether a coin was biased or not, where the biased one would have (say) 90% chance of heads, and having a credence about whether the coin is biased. In such a case the hypotheses would be chance-statements, and it does make sense to have credences over them.
However, it’s unclear to me whether we can view either the house example or AGI risk as involving objective chances. The most plausible interpretation of an objective chance usually involves a pretty clear stochastic causal mechanism (and some would limit real chances to quantum events). But if we don’t want to allow talk of objective chances, then all the evidence you receive about Smith’s electricity skills, and the probability that they built the house, is just more evidence to conditionalize your credences on, which will leave you with a new final credence over the proposition we ultimately care about: whether your house will burn down. If so, the levels wouldn’t make sense, I think, and you should just multiply through.
I’m not sure how this affects the overall method and argument, but I do wonder whether it would be helpful to be more explicit what is on the respective axes of the graphs (e.g. the first bar chart), and what exactly is meant by risk, to avoid risks of equivocation.