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Training a ML model to understand how "most sensible, considerate humans" would act is anything but straightforward. I'm not even sure most people would even co
by stolenmerch 5y ago
Training a ML model to understand how "most sensible, considerate humans" would act is anything but straightforward. I'm not even sure most people would even consider cropping an athlete at the jersey number regardless of race or gender - it just doesn't make sense yet the machine seemed to do it at the same rate for male vs. female. Retrofitting discrimination onto this result only once you learn the label of the data isn't particularly useful. We want to know how to make good non-harmful predictions in the future.
- bastawhiz 5y ago> Training a ML model to understand how "most sensible, considerate humans" would act is anything but straightforward. This is exactly why it's a challenge! The point is that the goal should be to do it the way a human would find satisfactory and not the way that's easy. Even if the machine wasn't trained to be biased, the machine should still produce results which people see as good. We didn't invent machines to do jobs just so people could say "but that's a bad result" and reply with "yes but the machine wouldn't know that". We should strive for better.