So, the college admissions cheating scandal has gotten people talking again about how "holistic" admissions are anti-meritocratic, originally designed to discriminate against Jews, and now discriminate against Asians.
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The fact that we *moved away* from meritocratic college admissions is yet another example of the way in which US society has optimization pressure towards *partiality* (favoritism, cronyism, whatever you want to call it.)
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Whenever you see avoidance of "fair tests", you are seeing partiality.
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For example, the policy "select people with strong resumes" allows avoidance of fair tests. People can reframe failure to look like success. It's hard to make an apples-to-apples comparison between people's overall life accomplishments.
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If there's no risk that you could fail, it's not a fair test.
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"do a clustering analysis of a dataset and find insights" is not a fair test -- you can always get *a* result. The interesting (=fair test) question is "can your model accurately recapitulate known truths better than the previous baseline?"
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One of LRI's compounds seems like it might be failing a fair test, and we told everyone: https://thelri.org/blog-and-news/interim-results-for-epitalon-study/ …. All advice on how to succeed would say we should NEVER do this.
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End of conversation
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