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If you train an AI using data from a system that already has certain biases, then the AI is going to replicate those same systemic biases in it's own predictions. It follows the "garbage in, garbage out" idiom.

Curiously though, did you compare the non-hire (full time) rates of interns vs fire rates of non-interns?

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>If you train an AI using data from a system that already has certain biases, then the AI is going to replicate those same systemic biases

That's not what happened in the example at all. The example company isn't biased against summer interns, "who stops working after x time" was just a bad question.

The comment you're replying to can boil down to "do you want a monkey's paw solving your problem? If so then AI may be for you"

Or perhaps "stop pretending you're ever going to get ethics or empathy out of a computer"

I was referring to the Amazon resume model. The intern hires model was labeling, as GP said.

>did you compare the non-hire (full time) rates of interns vs fire rates of non-interns?

Not sure I understand the question. IIRC, the way data was setup there was no way to tell why an intern stopped working for the company, because for all interns "reason code" for separation was the same.

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