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I think the troubling aspect here is that these are actually statistically sound decisions. The company does not need to have a strong implication that Hotmail -> bad driver. They only need to know that P(bad driver | Hotmail) > P(bad driver | Gmail). And through some correlation (many of the theories proposed by others seem plausible), this apparently holds.

As developers of these systems, we need to be careful of how we might apply superficial correlations like these, so that we don't cause harm and burden to those who happen to be caught up in them through no fault of their own.

As a side note, I happen to have a 10+ year old Hotmail account that I use for signing up to services. My Gmail address is only given out to real people. Personally, I view it as a testament to my diligence that I have managed to give my email out to hundreds of websites, several of which have had database breaches, and still only see one or two unwanted emails per week.




If insurance were a way to minimize risk by distributing it, differential pricing would be applied only for factors you can influence, to the degree that you can influence them. E.g. if you are a smoker, health insurance would be more expensive, but if you are willing to go into therapy, the insurance would pay for it to save on cancer treatments down the line.

Of course insurance companies are first and foremost trying to maximize their profits by charging everyone just slightly more than their expected payouts [1]. That also means that their profits go up when they get better at modeling someone's risk profile and then charge them more. The whole business model of insurance depends on treating people differently, even if they are different due to no fault of their own.

[1] corollary: if you have enough money, you shouldn't buy insurance (expected loss), but insurance companies (expected profit)


The more perfectly insurance companies can model future payouts, the less it acts like insurance.


You should never buy insurance.

The Expected loss with insurance is higher than without for two reasons:

* The insurance company must make a profit

* You are much more likely to go bankrupt than the insurance company. Bankruptcy limits the payout.

The only time you should take insurance is when you know something the insurance company doesn't. For example, you know how very dangerous your house electrics are, while they don't. Take that thinking too far and it's called "insurance fraud" though.


Like those who are of a certain gender and are charged more?[1]

Or those from a certain zip code who are charged more? [2]

Or those who drive a certain car model who are charged more? [3]

How is email address any different?

[1] https://www.nerdwallet.com/blog/insurance/teen-boys-high-car...

[2] http://www.latimes.com/business/autos/la-fi-hy-ten-worst-zip...

[3] https://electrek.co/2017/06/05/tesla-owners-insurance-rates/


Its much easier to change your email than your gender or zip code. Charging different amounts based on a car model makes sense though, some cars are much safer than others.


Yes, the people that STILL haven't bothered to change from Hotmail even though it's quite easy are more likely to not be good drivers. I don't think the difficulty of changing the factor should matter, in fact I'd say the easier they are to change the more impact they likely have as it may show laziness (higher risk). Having these factors be openly noted however would be important.


> we need to be careful of how we might apply superficial correlations like these

Well, for one, correlation != causation. Period.


Well, that's fine, but insurance companies are only interested in correlation. It's rather obvious that Hotmail addresses don't cause car accidents.


Yes, it is obvious, hence my comment. Using correlation as a stand-in for causation is stupid.


Why should an insurance company care about causation?


I never said insurance companies should care about it. Obviously some folks do care about it else this article would not be on the front page of HN, since the entire premise is "correlation may not be a good way to discriminate!!"


But does that mean that causation is a good way to discriminate or that the article is claiming anything like that?




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