[Certhas]

This is nonsense. Yes, given a prediction method that produces a probability, there is a tradeoff between false negative and positive. (Your example doesn't exhibit this though). But of course you can decrease your rate of false negative without affecting your fake positives: If you change your prediction method!

This is literally the first image in the relevant Wikipedia page:

https://en.m.wikipedia.org/wiki/Receiver_operating_character...

At a given acceptable level of false positives, different predictors have different rates of false negatives.

[pandaman]

This is what we get for replacing education with googling... Are you suggesting replacing interviews with some other metrics?

[mrjet]

He is suggesting that better methods of interviewing could increase “area under the curve”, improving both fase negative and false positive rates at once.

[pandaman]

I understand that. But even if you come up with a classifier that gives better false positives and false negatives rates than the existing process, it's still going to be tuned towards minimizing false positives and leaving false negative to grow freely. My point was that only companies that do that became successful enough to attract a whole lot of applicants who fail interviews and complain about them on social media.