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Simplified comment: Is weight the cause of increased costs and decreased safety? Yes. Is blackness the cause of non-payment? No. Original comment: I think t

Simplified comment: Is weight the cause of increased costs and decreased safety? Yes. Is blackness the cause of non-payment? No. Original comment: I think there's an important difference between directly consuming a resource, and being broadly lumped into a group that has an elevated chance of increased consumption as a proxy for other underlying factors: primarily socio economics. Why stop at that level of granularity when you can easily keep slicing. What's the comparison between college educated members of each group? What about children of professionals? I think the brutish imprecision of stopping at race, especially that it's almost certainly not the cause, makes it a very different argument. As an aside, wtf is the point of FICO if not to be a measure of precisely these outcomes?

yummyfajitas 10y ago
Suppose the airline had a scale which worked in the following way. It will measure their weight, but add randomly generated noise of a few pounds either way. Suppose this scale is perfectly adequate for the airline's purpose - i.e. it provides a sufficiently accurate measurement to provide safety - but it no longer provides a "true" measurement. Based on your reasoning, since this scale is no longer a perfect measurement of the true causal factor, it shouldn't be used. In reality, passenger weight can vary between weigh-in and getting on the plane. The passenger might eat food or use the bathroom before boarding. Weight on-plane, rather than at boarding time, is the true cause of decreased safety. Weight at boarding is merely a statistical approximation to this (and admittedly a very good one). Reality is uncertain, so it seems we are back to the same place. Tangentially: Why stop at that level of granularity when you can easily keep slicing...As an aside, wtf is the point of FICO if not to be a measure of precisely these outcomes? The reason for this is that using more factors is often legally problematic. Such factors are often correlated with race and regulators therefore treat using them as "redlining." For example, (income = low) AND (location == oakland) might also be predictive, but that's strongly correlated with race, and regulators will rape you if you try and use it for predictive purposes. Also, from what I've seen, your suggesting that slicing further will eliminate the predictiveness of race doesn't seem to be true. At the very least, we often need to slice much further than our available data sets allow. For example, FICO includes a lot of data but still doesn't make race non-predictive. Similarly, in education, race is still predictive after accounting for parental income - rich blacks do worse than poor Asians: https://en.wikipedia.org/wiki/Achievement_gap_in_the_United_States#Income_and_class https://en.wikipedia.org/wiki/Achievement_gap_in_the_United_... The idea that we can avoid these ethical questions by slicing data in the exact right way is probably not true. We should instead figure out our ethics so that we can do things right even if the data doesn't turn out the way we imagine.
- thinkloop 10y ago
  > From what I've seen, your suggesting that slicing further will eliminate the predictiveness of race doesn't seem to be true... The idea that we can avoid these ethical questions by slicing data in the exact right way is probably not true Are you taking the position that some races are genetically pre-disposed to these behaviors - that their race is the root cause of them? I personally don't think so, not because it would be an unpopular position (popularity has little place in a discussion of truth), but because I have seen little compelling evidence of it. If you agree, then you must also agree that slicing data around causes would necessarily provide better predictive metrics. In this day and age of intense data collection, I find it hard to believe that we can't do better than "Oakland" or "Asians" or "Blacks". You'd probably go bankrupt pretty soon while your competition does real data analysis. Still, it is undeniable that race - the way society is currently structured - can provide easy and useful proxy to a whole host of predictable behaviors. If we were econobots, concerned only with maximizing immediate economic gains, we'd maybe use it. But this is an old idea. We know we are paying slightly in short term economic benefits, for human social benefits, as well as long term economic benefits. That's why we have affirmative action, and mix low income housing with rich housing, and similar programs: because we discovered that ghettos are self perpetuating, and make it more difficult to change one's standing. So we pay a little now in economic effeciency for a better aggregate economic future.

yummyfajitas 10y ago

Suppose the airline had a scale which worked in the following way. It will measure their weight, but add randomly generated noise of a few pounds either way. Suppose this scale is perfectly adequate for the airline's purpose - i.e. it provides a sufficiently accurate measurement to provide safety - but it no longer provides a "true" measurement. Based on your reasoning, since this scale is no longer a perfect measurement of the true causal factor, it shouldn't be used. In reality, passenger weight can vary between weigh-in and getting on the plane. The passenger might eat food or use the bathroom before boarding. Weight on-plane, rather than at boarding time, is the true cause of decreased safety. Weight at boarding is merely a statistical approximation to this (and admittedly a very good one). Reality is uncertain, so it seems we are back to the same place. Tangentially: Why stop at that level of granularity when you can easily keep slicing...As an aside, wtf is the point of FICO if not to be a measure of precisely these outcomes? The reason for this is that using more factors is often legally problematic. Such factors are often correlated with race and regulators therefore treat using them as "redlining." For example, (income = low) AND (location == oakland) might also be predictive, but that's strongly correlated with race, and regulators will rape you if you try and use it for predictive purposes. Also, from what I've seen, your suggesting that slicing further will eliminate the predictiveness of race doesn't seem to be true. At the very least, we often need to slice much further than our available data sets allow. For example, FICO includes a lot of data but still doesn't make race non-predictive. Similarly, in education, race is still predictive after accounting for parental income - rich blacks do worse than poor Asians: https://en.wikipedia.org/wiki/Achievement_gap_in_the_United_States#Income_and_class https://en.wikipedia.org/wiki/Achievement_gap_in_the_United_... The idea that we can avoid these ethical questions by slicing data in the exact right way is probably not true. We should instead figure out our ethics so that we can do things right even if the data doesn't turn out the way we imagine.

thinkloop 10y ago
> From what I've seen, your suggesting that slicing further will eliminate the predictiveness of race doesn't seem to be true... The idea that we can avoid these ethical questions by slicing data in the exact right way is probably not true Are you taking the position that some races are genetically pre-disposed to these behaviors - that their race is the root cause of them? I personally don't think so, not because it would be an unpopular position (popularity has little place in a discussion of truth), but because I have seen little compelling evidence of it. If you agree, then you must also agree that slicing data around causes would necessarily provide better predictive metrics. In this day and age of intense data collection, I find it hard to believe that we can't do better than "Oakland" or "Asians" or "Blacks". You'd probably go bankrupt pretty soon while your competition does real data analysis. Still, it is undeniable that race - the way society is currently structured - can provide easy and useful proxy to a whole host of predictable behaviors. If we were econobots, concerned only with maximizing immediate economic gains, we'd maybe use it. But this is an old idea. We know we are paying slightly in short term economic benefits, for human social benefits, as well as long term economic benefits. That's why we have affirmative action, and mix low income housing with rich housing, and similar programs: because we discovered that ghettos are self perpetuating, and make it more difficult to change one's standing. So we pay a little now in economic effeciency for a better aggregate economic future.