-Give people photographs of various women and men and ask them to judge how tall the people in the photos are, as [Nelson _et al._ 1990 did](/papers/nelson_et_al-everyday_base_rates_sex_stereotypes_potent_and_resilient.pdf), and people's guesses reflect both the photo-subjects' actual heights, but also (to a lesser degree) their sex. Unless you expect people to be perfect at assessing height from photographs (when they don't know how far away the cameraperson was standing, aren't ["trigonometrically omniscient"](https://plato.stanford.edu/entries/logic-epistemic/#LogiOmni), _&c._), this behavior is just _correct_: men really are taller than women on average (I've seen _d_ ≈ 1.4–1.7 depending on the source), so P(true-height|apparent-height, sex) ≠ P(height|apparent-height) because of [regression to the mean](https://en.wikipedia.org/wiki/Regression_toward_the_mean) (and women and men regress to different means). But [this all happens subconsciously](TODO: "Peering Through Reverent Fingers"): in the same study, when the authors tried height-matching the photographs (for every photo of a woman of a given height, there was another photo in the set of a man of the same height) _and telling_ the participants about the height-matching _and_ offering a cash reward to the best height-judge, more than half of the stereotyping effect remained. It would seem that people can't consciously readjust their learned priors in reaction to verbal instructions pertaining to an artificial context.
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-Once you understand at a _technical_ level that probabilistic reasoning about demographic features is both epistemically justified, _and_ implicitly implemented as part of the way your brain processes information _anyway_, then a moral theory that forbids this starts to look much less compelling. Maybe a Bayesian superintelligence could redesign the human brain to _not_ use Bayesian reasoning when contemporary egalitarians would find that ideologically disagreeable? But a world populated by such people, constitutionally incapable of reacting to statistical regularities that we, in our world, automatically take into account (without necessarily noticing that we do), would likely come off as creepy or uncanny.
+Give people photographs of various women and men and ask them to judge how tall the people in the photos are, as [Nelson _et al._ 1990 did](/papers/nelson_et_al-everyday_base_rates_sex_stereotypes_potent_and_resilient.pdf), and people's guesses reflect both the photo-subjects' actual heights, but also (to a lesser degree) their sex. Unless you expect people to be perfect at assessing height from photographs (when they don't know how far away the cameraperson was standing, aren't ["trigonometrically omniscient"](https://plato.stanford.edu/entries/logic-epistemic/#LogiOmni), _&c._), this behavior is just _correct_: men really are taller than women on average (I've seen _d_ ≈ 1.4–1.7 depending on the source), so P(true-height|apparent-height, sex) ≠ P(height|apparent-height) because of [regression to the mean](https://en.wikipedia.org/wiki/Regression_toward_the_mean) (and women and men regress to different means). But [this all happens subconsciously](/2020/Apr/peering-through-reverent-fingers/): in the same study, when the authors tried height-matching the photographs (for every photo of a woman of a given height, there was another photo in the set of a man of the same height) _and telling_ the participants about the height-matching _and_ offering a cash reward to the best height-judge, more than half of the stereotyping effect remained. It would seem that people can't consciously readjust their learned priors in reaction to verbal instructions pertaining to an artificial context.