3 ms·

A deeper explanation is often desired when you decide to take your solution to the real world and impact peoples lives. I'm sure I'm not alone in the fear of l

A deeper explanation is often desired when you decide to take your solution to the real world and impact peoples lives. I'm sure I'm not alone in the fear of living in a (not so far) future dystopia where our driver's licences get cancelled because of our driving behavior caught on traffic cameras. "What did I do that caused it to get cancelled?", "We don't know, better luck next time." "Your test got flagged for cheating, it's a very sophisticated system with IR vision, audio detection and grade assessment, there's nothing I can do." "Our theft-preventing camera system says you are suspicious, you're gonna have to go now." When all we have to respond is "its the algorithm", I don't think much people will be ok with that. Youtube for instance has gone down this rabbit hole and a lot of content creators and users are less than happy to receive that answer.

ashelmire 8y ago
Those explanations are awful. The reasons are usually pretty simple and sound much more reasonable. The answer is not “I don’t know” in any of those cases - it’s going to be pretty clear that they share some similarity to other cases of theft, cheating, etc, and this will probably be obvious on examination, “our system flags people who are wearing masks” or “these 30 seconds of sounds are almost exactly the intro to song X”. Human review is also useful, but humans make plenty of errors as well (and the response is often “I don’t know why”!). I’d say these errors are not due to the inscrutable nature of ML but bad communication.