This is associative generalization at its best, also known as prejudice. Not all prejudice is bad, it is bad when you apply it against persons and irreapective

This is associative generalization at its best, also known as prejudice. Not all prejudice is bad, it is bad when you apply it against persons and irreapective of success rate,if even one individual causes the prejudiced test to fail, the test becomes an instrument of injustice. We all think prejudice in the context of race or sex,but it can be applied in just about any context. For example: You can be prejudiced when performing facial recognition and you may have some failure rate which can be tolerable for things like authentication or the police finding a suspect. But let's say this is used to perform an airstrike/assasination of a terrorist, is any level of failure rate acceptable? It shouldn't be,not on it's own. You should have independent corroboration and subjective confirmation. The 0.01% false positive times 7billion faces means a lot of people whose potential death you are tolerating. This is similar to why lie detector is inadmissible in court.even with dna evidence you need to independently prove either motive or plausibility of the crime being committed by the suspect. This is far from assasinations and criminal cases but the concept of "you're unethical if you tolerate any form of systemic injustice against even one person" To me this sort of ML matching should never be used in hiring decisions but it can be used to corroborate manager/peer evaluations/sentiment after a probationary period. In reality this means people have to lie/fake personality quizzes and verbage/vocabulary resulting in the best fakers outperforming the flawed yet better non-fakers. Very sad how history repeats.