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I don't get the impression that most people are afraid of irrelevant correlations, but rather relevant ones that nevertheless disproportionately affect certain

I don't get the impression that most people are afraid of irrelevant correlations, but rather relevant ones that nevertheless disproportionately affect certain populations. To use your Armenian example, it could be that while being Armenian doesn't actually affect your driving, a "true" model could still end up being bad for Armenians if being Armenian is correlated with the things that actually do affect crash risk. But what about this: we don't need to solve all social ills every single time. What if we let the algorithm correctly decide crash risk, and if we notice that it unduly impacts Armenians and that's not an outcome we want, we via a separate channel compensate the Armenians? That is, acknowledge the fact that Armenians may crash more, but give them a government subsidy to offset the higher premiums, and work to bring the premiums down (i.e.: fix the underlying issues that being Armenian is correlated with). It's related to something I've been thinking about lately with regard to minimum wage. I like the idea that everyone should have a livable income, but tying the implementation to businesses that have low wage jobs seems like mixing concerns. For example, my company doesn't have any minimum wage jobs, but shouldn't my company chip into this social ideal same as any other? What if we let the businesses pay whatever the market will bear, and if we decide that as a social concern people should get more than that, we subsidize them from the government, which is wear this concern is coming from in the first place.

radarsat1 10y ago
> That is, acknowledge the fact that Armenians may crash more, but give them a government subsidy to offset the higher premiums I'm just incredibly impressed that you came up with a way to make this sound even worse.. can you imagine the government giving subsidies to Armenian drivers with good records because insurance companies are overcharging them due to the statistical performance of their category? A tax benefit for being an outlier from the mean? My god. Not only does that sound like a terrible idea, but intensely complicated to organize in the general case. And what happens when an individual is in multiple categories? What if only gay Armenians who listen to disco are the at-risk driving category? How do we aggregate the tax subsidies, multiplicatively, additively, ..? This sounds like an administrative nightmare, I congratulate you on your evil ingenuity.
joncrocks 10y ago
I think that people are afraid of conditions that one can't control, and are correlated with negative characteristics. Lets say that I am male, and am young. Lets say that as a young male driver, I am likely to be a cause of an accident 20% of the time i.e. 1 in 5 young male drivers will cause an accident. Lets say as a young female driver, I am likely to be the cause of an accident 5% of the time. So on the face of it, young male drivers are riskier, and should pay appropriately. But lets say there was another measurable factor, such as a 'recklessness' score, which describes how recklessly you behave. And lets say that if you are 'reckless', you are likely to be the cause of an accident 90% of the time. And the maths works out that if you take the 'not reckless' group from men and women, they are equally likely to be the cause of an accident, i.e. there are more reckless men than women. If you were to take the stats at face value, then a good proportion of people are being over and under-charged, because you are using a proxy measure rather than digging deeper for a root cause/don't have the data available. I think this is exactly what people are afraid of, businesses/people being lazy and making assumptions that are correlative, based on characteristics that one can't change.
- yummyfajitas 10y ago
  No, that's not what this research is about at all. This research is about what happens when combining the recklessness score with gender-based distributions provides more accurate results than recklessness alone. Many people consider this to be "unfair". This article shows how much profit you need to sacrifice to get fairness.

radarsat1 10y ago

> That is, acknowledge the fact that Armenians may crash more, but give them a government subsidy to offset the higher premiums I'm just incredibly impressed that you came up with a way to make this sound even worse.. can you imagine the government giving subsidies to Armenian drivers with good records because insurance companies are overcharging them due to the statistical performance of their category? A tax benefit for being an outlier from the mean? My god. Not only does that sound like a terrible idea, but intensely complicated to organize in the general case. And what happens when an individual is in multiple categories? What if only gay Armenians who listen to disco are the at-risk driving category? How do we aggregate the tax subsidies, multiplicatively, additively, ..? This sounds like an administrative nightmare, I congratulate you on your evil ingenuity.

joncrocks 10y ago

I think that people are afraid of conditions that one can't control, and are correlated with negative characteristics. Lets say that I am male, and am young. Lets say that as a young male driver, I am likely to be a cause of an accident 20% of the time i.e. 1 in 5 young male drivers will cause an accident. Lets say as a young female driver, I am likely to be the cause of an accident 5% of the time. So on the face of it, young male drivers are riskier, and should pay appropriately. But lets say there was another measurable factor, such as a 'recklessness' score, which describes how recklessly you behave. And lets say that if you are 'reckless', you are likely to be the cause of an accident 90% of the time. And the maths works out that if you take the 'not reckless' group from men and women, they are equally likely to be the cause of an accident, i.e. there are more reckless men than women. If you were to take the stats at face value, then a good proportion of people are being over and under-charged, because you are using a proxy measure rather than digging deeper for a root cause/don't have the data available. I think this is exactly what people are afraid of, businesses/people being lazy and making assumptions that are correlative, based on characteristics that one can't change.

yummyfajitas 10y ago
No, that's not what this research is about at all. This research is about what happens when combining the recklessness score with gender-based distributions provides more accurate results than recklessness alone. Many people consider this to be "unfair". This article shows how much profit you need to sacrifice to get fairness.