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The Economist on using phrenology for hiring and lending decisions: "Some might argue that face-based analysis is more meritocratic" […] "For people without access to credit, that could be a blessing" (lemmy.ml)
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cross-posted from: https://lemmy.ml/post/38830374

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economist article

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https://en.wikipedia.org/wiki/Phrenology

excerpts of economist article:

screenshot of text "Imagine appearing for a job interview and, without saying a single word, being told that you are not getting the role because your face didn’t fit. You would assume discrimination, and might even contemplate litigation. But what if bias was not the reason? What if your face gave genuinely useful clues about your probable performance at work? That question is at the heart of a recent research"

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screenshot of text "a shorter one. Some might argue that face-based analysis is more meritocratic than processes which reward, say, educational attainment. Kelly Shue of the Yale School of Management, one of the new paper’s authors, says they are now looking at whether AI facial analysis can give lenders useful clues about a person’s propensity to repay loans. For people without access to credit, that could be a blessing."

screenshot of a tweet from @TheEconomist on Nov 8 2025 with text "What if your face gave genuinely useful clues about your probable performance at work? That question is at the heart of a recent paper"
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[–] 2 points 11 months ago (2 children)

I cant imagine a model being trained like this /not/ end up encoding a bunch of features that correlate with race. It will find the white people, then reward its self as the group does statistically better.

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  • [–] 2 points 11 months ago (1 child)

    Even a genuinely perfect model would immediately skew to bias; the moment some statistical fluke gets incorporated into the training data that becomes self re-enforcing and it'll create and then re-enforce that bias in a feedback loop.

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  • [–] 2 points 11 months ago

    Usually these models are trained on past data, and then applied going forward. So whatever bias was in the past data will be used as a predictive variable. There are plenty of facial feature characteristics that correlate with race, and when the model picks those because the past data is racially biased (because of over-policing, lack of opportunity, poverty, etc), they will be in the model. Guaranteed. These models absolutely do not care that correlation != causation. They are correlation machines.

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