steven0461 comments on Bayesian Adjustment Does Not Defeat Existential Risk Charity - Less Wrong
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Karnofsky has, as far as I know, not endorsed measures of charitable effectiveness that discount the utility of potential people. (On the other hand, as Nick Beckstead points out in a different comment and as is perhaps under-emphasized in the current version of the main post, neither has Karnofsky made a general claim that Bayesian adjustment defeats existential risk charity. He has only explicitly come out against "if there's even a chance" arguments. But I think that in the context of his posts being reposted here on LW, many are likely to have interpreted them as providing a general argument that way, and I think it's likely that the reasoning in the posts has at least something to do with why Karnofsky treats the category of existential risk charity as merely promising rather than as a main focus. For MIRI in particular, Karnofsky has specific criticisms that aren't really related to the points here.)
While valuing potential persons at 0 makes existential risk versus other charities a closer call than if you included astronomical waste, I think the case is still fairly strong that the best existential risk charities save more expected currently-existing lives than the best other charities. The estimate from Anna Salamon's talk linked in the main post makes investment into AI risk research roughly 4 orders of magnitude better for preventing the deaths of currently existing people than international aid charities. At the risk of anchoring, my guess is that the estimate is likely to be an overestimate, but not by 4 orders of magnitude. On the other hand, there may be non-existential risk charities that achieve greater returns in present lives but that also have factors barring them from being recommended by GiveWell.
Actually, according to this transcript on page four, Holden finds that the claim that the value of creating a life is "some reasonable" ratio of the value of saving a current life is very questionable. More exactly, the transcript sad: