Earlier quoted context omitted.
It literally mathematically requires a lower bar. If your criteria for hiring a software engineer is that an applicant demonstrate proficiency in a given set of tasks, for example, you will result in a pool of applicants sorted by proficiency. Say we have 100 slots open to hire people, and we would normally just take the top 100 applicants according to this proficiency criteria. Now, introducing any criteria which re…
This assumes that the existing system is already a perfectly ordered meritocracy. Affirmative action supporters are coming at it from a different hypothesis: that 20 of the applicants pass the objective proficiency bar, and now people will apply subjective criteria like "culture fit", who has "leadership potential", etc, and this is where bias creeps in. Somehow the qualified candidates from certain groups don't make…
This would no longer be the example I described. This is why people who support something approximating a meritocracy are typically in favor of any efforts to remove bias, and move in a direction of blind hiring. It is disingenuous to say that this is the objective of those in favor of affirmative action, however, as color/gender blindness is not their goal at all. The example here of getting rid of the SAT is a perfect example of that.
> you believe that all groups are equally capable, and differences in outcomes indicate how much bias is left to overturn
And this is the fundamental difference. Advocates of affirmative action/CRT believe that different population outcomes can be used as a de facto post hoc rationalization that the system which produced the outcomes must be necessarily biased in favor or against the groups. This is fallacious thinking. The conclusion doesn't even follow your own premise, and your premise is simply an assertion of what you believe to be true.
"[I] believe that all groups are equally capable, therefore differences in outcomes indicate that systems are biased." This is a fallacious statement. Capability is a minor, minor portion of the equation. Interest, culture, behavior, geography, income, wealth, history... Where do these fit into your model?
Let me tell you something about hiring. I've been responsible for hiring engineers on many occasions, and still am. If I were instructed to achieve, for example, 50/50 parity between male and female engineers: I would have to hire 100% of the female engineer applicants. If I were instructed to make sure that 13% of the engineers were black (to be in line with population levels): I would have to hire 100% of the black engineer applicants.
Your de facto reasoning that the reason that engineers are overwhelmingly white/east Asian/Indian/Eastern European is that the hiring system is favored as such. The pool of applicants, however, skews even further towards this representation. Almost all companies are already trying to capture a greater proportion of other demographics, and are simply unable to do so. But in regards to sacrificing proficiency, if you understand the proportionality of the applicant pool, your argument of not sacrificing proficiency completely falls apart. It's as I said, if I were to get 50/50 female representation, I would literally have to get rid of proficiency criteria altogether and literally hire every woman on the spot. It would absolutely be a massive hit to proficiency. That isn't saying that women are less proficient at engineering.
Lastly, I'm curious if you care about this for anything else. For example, Indians are extremely over represented in medicine as compared to their population. Filipinos are extremely over represented in nursing as compared to their population. Because you believe all group are equally capable, you surely believe that a cabal of Filipino nurses and their in group preferences are responsible for maintaining the hegemony of Filipino nurse supremacy, correct?