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But that's how sexism and racism and any kind of discrimination works in the human mind. There are innocuous traits, like sex and skin color, but also many oth

But that's how sexism and racism and any kind of discrimination works in the human mind. There are innocuous traits, like sex and skin color, but also many other traits, and we use them to make predictions about behavior. This is unfair discrimination; fair discrimination looks directly at the behavior of an individual, not some innocuous proxy for the behavior, even if that proxy is right 80% of the time. For example, most people who have an account throwawayXXX on HN are using it temporarily to say something. However, I actually kept one such account for a long time. My username is an innocuous trait, but you're discriminating if you assume my motives and behavior will be like most of the other throwaway accounts, just based on my username. That said, I believe unfair discrimination is unavoidable in life because we can't always wait around to see what an individual's behavior will be before we discriminate fairly. We couldn't function without stereotypes and assumptions. And so, while still unfair because of the potential for unjustified penalties, it's much better to look at many variables than it is to look at one or two.

current_call 11y ago
Under that argument, using machine learning to make decisions on things like advertising campaigns or mortgage rates is still bad even if race and sex do not correlate. That's a much different argument than what the author is making. It's a decent argument too. That said, I believe unfair discrimination is unavoidable in life because we can't always wait around to see what an individual's behavior will be before we discriminate fairly. We couldn't function without stereotypes and assumptions. And so, while still unfair because of the potential for unjustified penalties, it's much better to look at many variables than it is to look at one or two. Then you think machine learning is the best solution that exists?
- ectoplasm 11y ago
  Thanks, I thought your points were good too. I think machine learning in combination with human oversight is the least bad. Machines can help eliminate human bias, and humans can pick up on things that the machines missed. I wouldn't trust a machine to lend out my money, but I would trust it to give me a lot of data about someone and make recommendations on that basis. When you search Google, you get a mostly impartial set of results, but then you need to choose the most relevant from the top 10. Without Google, you'd visit far fewer websites based only on your accumulated experience. Without a human to filter through the top 10, you'd have to systematically read webpages in order until you found what you wanted. Maybe the example is a bit stretched, but machines and humans cooperating seems to work well, even right on the HN front page, or in the browser spellchecker as I type this message (apparently HN is not a word).

current_call 11y ago

Under that argument, using machine learning to make decisions on things like advertising campaigns or mortgage rates is still bad even if race and sex do not correlate. That's a much different argument than what the author is making. It's a decent argument too. That said, I believe unfair discrimination is unavoidable in life because we can't always wait around to see what an individual's behavior will be before we discriminate fairly. We couldn't function without stereotypes and assumptions. And so, while still unfair because of the potential for unjustified penalties, it's much better to look at many variables than it is to look at one or two. Then you think machine learning is the best solution that exists?

ectoplasm 11y ago
Thanks, I thought your points were good too. I think machine learning in combination with human oversight is the least bad. Machines can help eliminate human bias, and humans can pick up on things that the machines missed. I wouldn't trust a machine to lend out my money, but I would trust it to give me a lot of data about someone and make recommendations on that basis. When you search Google, you get a mostly impartial set of results, but then you need to choose the most relevant from the top 10. Without Google, you'd visit far fewer websites based only on your accumulated experience. Without a human to filter through the top 10, you'd have to systematically read webpages in order until you found what you wanted. Maybe the example is a bit stretched, but machines and humans cooperating seems to work well, even right on the HN front page, or in the browser spellchecker as I type this message (apparently HN is not a word).