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Notes -
People don't typically use the term "anti-bias" to reference fixing bias in the statistical sense. It nearly always means preventing an AI from making correct hate-fact predictions or generating disparate outcomes based on accurate data.
Examples:
Lending algos/scores (e.g. FICO) are usually statistically biased in favor of blacks and against Asians - as in, a black person with a FICO of X is a worse credit risk than an Asian person with the same FICO. This is treated as "biased" against blacks because blacks tend to have lower FICO scores.
COMPAS, a recidivism prediction algo, correctly predicted that "guy with 3 violent and 2-nonviolent priors is a high recidivism risk, girl who shoplifted once isn't". That's "biased" because blacks disproportionately have a lot more violent priors. (There's also a mild statistical bias in favor of blacks, similar to the previous example.)
Language models which correctly predict the % of women in a given profession (specifically, "carpenter" has high male implied gender, "nurse" high female implied gender, and this accurately predicts % of women in these fields as per BLS data) are considered "biased" because of that accurate prediction.
(Can provide citations when I'm not on my phone.)
All of the examples you describe are simply examples of "making more accurate predictions", and that is totally not what the AI bias field is about.
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