Now that things have died down a bit, I want to point out that using this tweet as evidence has (extremely) obvious logical and empirical flaws.
Logically, drafting a document with pen and paper does not preclude later AI additions. Indeed, the story I originally sketched out in the post, with some sections written by the Pope and senior Vatican officials not using AI, and other sections with other senior Vatican offiicals heavily involving AI use, is fully consistent with a first draft written by pen and paper.
Empirically, I don’t think this has really been demonstrated.
The Atlantic and Snopes have both reached out to Christopher to substantiate his claims, and no evidence has been given so far.
Still, this has not stopped people from claiming that “an editor” of the encyclical has “confirmed” that I’m wrong, including in attempted community notes.
Ironically, the ppl screaming about how my careful multipronged analysis is “no evidence” consistently fell for obvious lies.
A post I’ve tried and failed to write many times is explaining what I tentatively call “frame errors”, as a third category of mistake distinct from logical fallacies and empirical errors. The basic idea is that your framework for reasoning about a problem is missing something structural which essentially invalidates your results, without making any apparent local logical errors or empirical mistakes.
This mistakes can be either by a single person or multi-player. If the latter, the core cognitive meta-mistake that reviewers or third parties make is that they’re too willing to accept the frame of whoever proposed the problem. Instead, you should be taking somebody’s facts as given (at least tentatively), but be much more suspicious of their framing and analytical choices.
Some examples I wanted to use in a post (I don’t justify why below), as subcategories of the overall issue.
Scope Mismatch
Corporate greed (constant) explaining price changes (varying)
Evolutionary psychology (millennia timescale) explaining 50-year behavioral shifts
American-specific factors explaining global phenomena
Survivorship Bias
Wald and the bombers
Studying successful startups to learn what makes startups succeed
Lossy Abstraction
CE Delft cultured meat: spreadsheet treats coupled physical parameters as independent, producing a physically impossible result
SIR models treating transmission rate as fixed, dropping the behavioral feedback loop that determined COVID’s actual qualitative shape (long plateaus at R≈1 rather than sharp peak and decline)
Conditioning on a Selected Variable
StarCraft scouting pillars: mapmakers optimize for balance, so equal win rates in existing matches with/without pillars can’t provide evidence for whether pillars work.
“SAT scores don’t predict college grades”—but colleges select on SATs, compressing the range. So the observed result can only say whether colleges select correctly for SAT scores.
Local-to-Global Projection
LeetCode: individually beneficial, but if everyone does it, hiring bar shifts and you’ve added a costly signal conveying no information
Standing up at a concert
Burning money: individual wealth decreases but remaining money appreciates
Evidence Consistent with Negation
Median Researcher Problem: fields with higher mean/median IQ replicate better. Does this imply median researchers cause higher replication rates? No, in practice higher median implies higher tail — evidence can’t distinguish “median researchers matter” from “top researchers matter”
When I first saw the error in the post I thought it was really obvious, but I’ve since updated that it’s actually somewhat hard to understand, for reasons I’d want to explore in a full post. Once you’re stuck in a frame, peering outside it is nontrivial!
Meta-Streetlight Bias
Spencer Greenberg treating situations where single data points are most informative as peripheral, because his statistical inference framework is a poor fit for a regime where you don’t yet have a model. This despite there in-practice being many situations where the first data point is by far the most important.
Anyway, this is a post I’ve tried and failed to write many times. It’s just too annoying to illustrate the meta-point with enough examples, especially since the object-level examples are themselves all explanations of frame errors that themselves are nontrivially hard to explain. If anybody else wants to take a stab at it, be my guest.
(A related angle worth tackling is figuring out whether we expect this type of reasoning to be easy or difficult for AIs. For a while I experimented with trying to build a scaffold or Claude skill instead of a blogpost to have identify frame errors, but never really got something working).