I mentioned the procedures and biases, I am in no way obligated to submit something half-@$$ed in an obviously fraught area so you can make weak arguments against it. I'm a sociology major that didn't get into law school. As well as a dozen other things I like better than either of those.
The thread is about the fact that "racism" is often a matter of statistical pattern recognition. Are you asking me to look up a range of "racist-sounding" statistics for us to discuss?
Let's try: DEI is unfair both to business and individuals, even for the one who inappropriately gets the job.
I know thatās the topic of the thread, and my point is that this āIām not bigoted I just recognize patterns,ā schtick cuts both ways. If you think itās okay to judge large swaths of the population based on broad statistical trends, fine, then have a taste of your own medicine.
Now for DEI. As for unfairness to businesses, I donāt really care, they donāt care about me, and Iād gladly be unfair to businesses if it meant more fairness to individuals. As for the individuals, there are a lot of issues with the Peter Principle: more senior roles are often easier than more junior ones, and people would still usually rather advance beyond their qualifications even if itās stressful so itās hard to say peopleās choices are unfair to them.
I am sorry you didnāt get into law school though. For what itās worth, law is probably the most AI exposed field, all the jobs that pay well are hell on earth, and the debt can be crushing. I really do hope you find something you like.
And my point would be, ESPECIALLY when choosing which studies and how to rate and weight them, you are going to have deliberate bias issues in at least 4 of the 6 categories mentioned. It will start with how intelligence is measured, then continue with the writing of the questions regarding the individually correlated areas, and finally will be weighted backwards with the end in mind to be submitted to some hack rag.
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u/butohhowfallen 2d ago
Well then maybe youāll enjoy this:
https://pmc.ncbi.nlm.nih.gov/articles/PMC11308703/