thinking abt once when a discussion abt bias in ML came up and an ML engineer was like "i don't see how there's a problem with ML or the model, it's just an issue with the training data"https://twitter.com/nicolaskb/status/1244921742486917120 …
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yeah i see how it's distinct from the amazon example below but i'd argue it's still a training set issue. can't say if the model is overfitting here — maybe it's learning everything it's fed at the correct level but w/o data bias we wouldn't have told it to learn this at all
either way models and their training data are inextricable from each other and u gotta check both to make sure they make sense
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turns out a lot of the same people who say "lol who cares about interpretability, the only thing that matters is out of sample performance" don't rly care when it turns out that the out of sample performance is Bad
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An underrated (but obviously not the only) part of the model fairness problem is the models being bad!
End of conversation
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