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Basing your entire decision on something as simple as the applicant's income will presumably produce outcomes that differ with race. Is that what you mean by ra

Basing your entire decision on something as simple as the applicant's income will presumably produce outcomes that differ with race. Is that what you mean by racist? Or do you mean the system identifies some individuals as high credit risk when they aren't and may actually lose money versus a more colorblind algorithm?

mlyle 6y ago
What's racist is squishy. Many people would say that identifying people who spend a lot of money at bars or casinos as a credit risk isn't racist, even if it happens to pick up more minorities. The mechanism for the credit risk and behavior seems tightly correlated. Many people would also say that identifying people who spend some money at clothing retailers that market to minorities as a credit risk is racist. Here, the relationship seems like a hidden way to spot minorities, who happen to be more of a credit risk. When banks drew red lines around all the minority neighborhoods and didn't lend to people there, because they couldn't consider race anymore, most thought this behavior unacceptable, even though it did genuinely reduce credit risk for banks. ML can't explain its rationale, and very often does the exact same thing inside an opaque box.
- unishark 6y ago
  Presumably black people who are great loan customers would be incorrectly refused by a system based on fashion or addresses alone, so your answer seems to be that yes, racist methods actually lose the bank money. My tendency is to think that since it is optimized to maximize profit, the more features about a person we get in the data, the less "racist" the system becomes under this definition. Yes it's more possible to "hide" racism in a complex method using tons of features, but increasingly less likely it would happen. If we can use income and debt and whatever other things to make either a good classifier that ignores race, or an inferior one that sneakily identifies race and bases the decision on that, mathematical optimization of the model should result in the former. ML can be trusted more than humans in this situation, not less. Of course there's still the issue of the data being biased, which is where it all started.
  - mlyle 6y ago
    > Presumably black people who are great loan customers would be incorrectly refused by a system based on fashion or addresses alone, Strawman. The question is whether predictions are improved by using race (or direct proxies for race), not whether it'd be wise to use proxies for race alone. Loan default rates are higher for disadvantaged minorities, even after controlling for many, many other variables (income, neighborhood, level of education, etc). Therefore, using race (or inferring race) improves prediction quality, but is ethically dubious.
    - unishark 6y ago
      > Strawman. Can we skip this kind of snippy arguing please? Anyway I take it you agree the statement is true but don't want to. So now you're saying those great minority loan customers are impossible to identify? I think you just need to figure out what information is still missing. What's the effect size at this point anyway?
      - mlyle 6y ago
        > > Strawman. > Can we skip this kind of snippy arguing please? If you don't start off by willfully mischaracterizing your opponent's argument in order to be able to more easily refute it, I think you'll find that they accuse you of this less. I think you're just willfully missing the point. Ideally, you'd just lend money to the people who will pay you back. Unfortunately, we can't predict this perfectly. Adding race, or proxies for race, to the things you consider improves your predictions somewhat.
        unishark 6y ago
        Nope it was a completely honest question and what I considered a constructive line of thought. You should assume good faith. I asked about two competing definitions of what is racist and you seemed to prefer one over the other. And what have I claimed to "easily refute" exactly? More like I ran with the definition, and considered how to address the problem as stated. I said more features were needed, I didn't say enough features were currently used. You keep pointing to a dichotomy between perfect and flawed, while I was talking about relative improvements. There isn't even necessarily a disagreement there.

mlyle 6y ago

What's racist is squishy. Many people would say that identifying people who spend a lot of money at bars or casinos as a credit risk isn't racist, even if it happens to pick up more minorities. The mechanism for the credit risk and behavior seems tightly correlated. Many people would also say that identifying people who spend some money at clothing retailers that market to minorities as a credit risk is racist. Here, the relationship seems like a hidden way to spot minorities, who happen to be more of a credit risk. When banks drew red lines around all the minority neighborhoods and didn't lend to people there, because they couldn't consider race anymore, most thought this behavior unacceptable, even though it did genuinely reduce credit risk for banks. ML can't explain its rationale, and very often does the exact same thing inside an opaque box.

unishark 6y ago
Presumably black people who are great loan customers would be incorrectly refused by a system based on fashion or addresses alone, so your answer seems to be that yes, racist methods actually lose the bank money. My tendency is to think that since it is optimized to maximize profit, the more features about a person we get in the data, the less "racist" the system becomes under this definition. Yes it's more possible to "hide" racism in a complex method using tons of features, but increasingly less likely it would happen. If we can use income and debt and whatever other things to make either a good classifier that ignores race, or an inferior one that sneakily identifies race and bases the decision on that, mathematical optimization of the model should result in the former. ML can be trusted more than humans in this situation, not less. Of course there's still the issue of the data being biased, which is where it all started.
- mlyle 6y ago
  > Presumably black people who are great loan customers would be incorrectly refused by a system based on fashion or addresses alone, Strawman. The question is whether predictions are improved by using race (or direct proxies for race), not whether it'd be wise to use proxies for race alone. Loan default rates are higher for disadvantaged minorities, even after controlling for many, many other variables (income, neighborhood, level of education, etc). Therefore, using race (or inferring race) improves prediction quality, but is ethically dubious.
  - unishark 6y ago
    > Strawman. Can we skip this kind of snippy arguing please? Anyway I take it you agree the statement is true but don't want to. So now you're saying those great minority loan customers are impossible to identify? I think you just need to figure out what information is still missing. What's the effect size at this point anyway?
    - mlyle 6y ago
      > > Strawman. > Can we skip this kind of snippy arguing please? If you don't start off by willfully mischaracterizing your opponent's argument in order to be able to more easily refute it, I think you'll find that they accuse you of this less. I think you're just willfully missing the point. Ideally, you'd just lend money to the people who will pay you back. Unfortunately, we can't predict this perfectly. Adding race, or proxies for race, to the things you consider improves your predictions somewhat.
      - unishark 6y ago
        Nope it was a completely honest question and what I considered a constructive line of thought. You should assume good faith. I asked about two competing definitions of what is racist and you seemed to prefer one over the other. And what have I claimed to "easily refute" exactly? More like I ran with the definition, and considered how to address the problem as stated. I said more features were needed, I didn't say enough features were currently used. You keep pointing to a dichotomy between perfect and flawed, while I was talking about relative improvements. There isn't even necessarily a disagreement there.