LLM written.
At this point, I've done enough policy work with AIs that my concern about hallucinated statistics is... reduced? I don't know what the right word is. I know what kinds of extended setups will get them to say crazy things and I know what kinds of narrow questions will generate genuinely reasonable and interesting answers. Meanwhile, I also know that every policy position paper ever written by a human has abused statistics in one way or another. So: six of one.
It would seem to me that the addition of the LLM to the mix is likely to result in output that is more likely to be aligned to the human writing it and less likely to be aligned with me, as the reader, conditional on the human being the kind who abuses statistics one way or another.
But everyone's epistemics are different. Reality will tell us over time where we land with our methods.
1. Unverifiable source ("a meta-analysis", trying to get authority without actually having it.
2. Hyper-specific numbers without error bars (and source), pretending to have certainty and precision.
3. Shockingly large number, intended to get an emotional reaction and a sense that it rounds to "all".
4. Numbers align too neatly with the point being made. They could still be true, but there could also be some overinterpreting, stretching of the truth or just plain lying going on.
Beautiful demonstration that numbers intended to convince are never to be trusted outright. No notes.
As an aside, the whole freak-out about LLM hallucinations has mostly shown me that way too many people accept human numbers way too easily. People are frequently as dumb or even dumber than LLMs.