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I'd say there's a one really important difference between being Black and using Hotmail, and that difference is the same reason why one is a protected class and one isn't.

The real concern here isn't discriminating against people with Hotmail accounts. On the surface level, there's nothing wrong with that. This does become an issue if the property of having a Hotmail account is used as an avenue to discriminate against people based on their membership in a protected class -- a sort of actuarial parallel construction, if you will. At that point, we do have a bit of a problem.

> We need regulation in place to stop any punitive decision making, public and private, which can be found in court to be based on correlation instead of causation.

So, young men could not be charged more for insurance than young women? In fact, young people in general would have to get the same price as everyone else? People in New York would have to pay the same as someone in rural Alaska? Someone who's been driving a year gets charged the same as someone who has been driving fifteen, etc.? Even being in an accident doesn't cause you to get in another accident. It's just correlated with that outcome. Throw out the entire actuarial aspect of insurance? These "punitive" charges are all based on correlation. I don't see this happening.

----

That said, as a non-statistician, I'm interested in what kinds of tests can be run on signals to verify that they're not proxies for selecting and discriminating against protected classes. Is there a way to modify a signal using its correlation with membership in these classes such that you can marginalize the attributes you're not allowed to discriminate based on?

For example, it seems reasonable to believe that hotmail use is correlated with age, race, gender, etc. As an insurance company, I would have information on hand to inform me as to the extent of this correlation, at least among my customer base, and I would in turn know the correlations of those factors with risk. Could I somehow remove the racial, gender, and age components of the Hotmail risk signal to obtain a signal that only conveys the portion of the risk correlation that is not based on those classes? If so, what is that statistical technique called?




If you can explain why it is OK for car insurance to charge differently by gender, but by race is not OK, I will be impressed.


Because they are different things (in the US).

Racial discrimination requires "strict scrutiny" to be legal.

https://en.wikipedia.org/wiki/Strict_scrutiny

Sex discrimination requires "intermediate scrutiny" to be legal.

https://en.wikipedia.org/wiki/Intermediate_scrutiny

This podcast gives a wonderful history:

https://www.wnyc.org/story/sex-appeal/


I can't say for certain, because I wasn't involved in the decision. ;) My gut reaction is that it only ends up being seen as OK because the group being penalized has historically been at an advantage in most other areas financially. I am aware that some European countries are moving to do away with charging by gender.


Not some - as far as I am aware, all of EU has now banned charging men more for car insurance.


Not only car insurance, but also eg life insurance. Men used to pay up 50% more, now they pay less and women pay more - all tariffs are "unisex" now.


The US did the same thing with health insurance. It seems the moral debate is over; charging different rates for insurance based on gender has been deemed discrimination throughout the first world.

All that remains to do in the US is find the political will to outlaw discrimination that favors women.


Well, then women will be forced to pay more than their fair share for car insurance, putting them at even more of a disadvantage. Right?


And this observation is downvoted because? It's just stating obviously true facts as far as I can tell.

edit And then I get downvoted for sticking my neck out. Let me break the parent post down.

It asserts that charging genders equally will shift more burden to women. This is clearly true because premiums are higher for men because we have more accidents (so much for all the female driver jokes).

It also asserts women were a previously disadvantaged class. Also clearly true.

So to the downvoters out there, you might have found something disagreeable about the parent post, but you're wrong and he's right.


I see two things wrong in the post:

> fair share

Male drivers and female drivers don't have a "fair share" of insurance costs. No one should be obligated to pay for the actions of others simply because they're the same sex.

Driving risk should be evaluated on an individual basis based on those individuals' behavior.

> putting them at even more of a disadvantage

Young women are no longer at a disadvantage. Young women graduate college more often and earn more than young men.

I focus on young women and young men in this case because those are the people most affected by gender discrimination in auto insurance pricing, because they lack individual driving records on which to base insurance rates.


I've heard there's an app for that in the US, you drive with it for a week or two and then they adjust your premiums according to how you drive. That would be a more fair system. We're not discussing that though, we're discussing the current system vs one where insurance companies can't discriminate on the basis of gender - clearly that advantages men unfairly and disadvantages women unfairly. Yeah the system is still unfair the way it is, but this change would make it more so.

Women graduate from college more often, but college is not what it once was - I'm a college dropout and I'm sure I earn more than most graduates (plus I don't have the debt.) Women still earn less than men. Your claim that they earn more is just false.



That's interesting, although as those sources indicate, the pay gap increases with age so that by the time women hit their mid thirties they're substantially behind men in compensation (for the same work.) It's an improvement to be sure, but I wouldn't go so far as to call the problem solved and say that women aren't still a disadvantaged class, when it comes to compensation.


Look at my other reply to the parent post for my explanation as to why I disagree.


I suspect few HN users could provide sound logic to support their anonymous downvotes. Downvotes are good — at least it means there’s a chance someone eventually modulates their current viewpoint.


Don’t worry about downvotes; oppression by the status quo for random and varying reasons is a discriminatory mechanism to make our words invisible. Keep commenting!


This is a common mistake. Reducing inequality is not morally equivalent to penalizing the person who was previously in a superior position.


The issue here is the phrase "fair share" - why is it a fair share? Because women get into fewer accidents? Cool, what if we get out of the statistical data that black people have more accidents(this is just an example, I don't know if they really do)? Should it be justified to charge them more?

But maybe if you look beneath the surface you will find out that minorities are more likely to drive older, less safe cars, which leads to them having more accidents - they don't have more accidents because they are black, they have more accidents because of other factors.

The same with men and women - I think it's fair to say that men drive more powerful, larger cars(on average) than women do. So....they get into more accidents because the cars they drive are objectively more difficult to drive. Which obviously means that you should calculate your premium based on the type of vehicle being insured, not on the gender of the driver - and, by extension, insurance for a man and a woman, on the same vehicle, should be identical. Penalizing a man in this situation because men as a group get into more accidents is absolutely unfair.


> I think it's fair to say that men drive more powerful, larger cars(on average) than women do

I know you're sorta spitballing here, but at least in my experience that's a bit backwards. Around here, I'd guess women drive SUVs more than men do. Growing up, fathers would buy their daughters SUVs because they were safer. Now, husbands have their wives drive the family SUV for the same reason. Plus, moms and minivans, etc. That might just be a thing in the area I grew up, though. It's probably different in downtown Seattle.

> So....they get into more accidents because the cars they drive are objectively more difficult to drive.

I'd bet the testosterone doesn't help much, either :-)


Maybe it's different in US, but over here in UK with everyone I know the ratio I see is a man with an SUV/large powerful sedan, woman with a small city car to get to work and back. Moms in minivans isn't as much of a stereotype because kids just walk to school or take the bus/metro. I suspect it's more common in rural areas but in my experience taking kids to school by car is still not the primary way of getting to school. But obviously that's just my experience, my perspective.

I'd love to see actual data on this , especially power and size of cars grouped by gender.

>>I'd bet the testosterone doesn't help much, either :-)

Women have their own behaviour-affecting hormones too, you know.


Moms in minivans isn't as much of a stereotype because kids just walk to school or take the bus/metro

Where in the UK are you? I lived several years in England as a teenager and went to a 'nice' school in the suburbs of Surrey and almost everybody drove their kids to school.


over here in UK with everyone I know the ratio I see is a man with an SUV/large powerful sedan, woman with a small city car to get to work and back. Moms in minivans isn't as much of a stereotype

Also in the UK and can report that mothers driving “Chelsea tractors” is totally a thing.


Sure, but most mothers in the UK are not driving Range Rovers :-) It's very much a rich people thing. Working-class mothers don't drive massive SUVs in general, even if they drop off their kids at school - tiny city cars seem far more common than tanks.


Heh. It's always interesting seeing the differences between countries.

> Women have their own behaviour-affecting hormones too, you know.

Of course. I was making a half-joke.


> I'd bet the testosterone doesn't help much, either :-)

Whoa there. Someone might infer from this that you believe men might generally make decisions / respond to situations differently than women with biological reasons being a contributing factor. That's a dangerous line of thought.


I think it's fair to say that men drive more powerful, larger cars(on average) than women do. So....they get into more accidents because the cars they drive are objectively more difficult to drive. [...] Penalizing a man in this situation because men as a group get into more accidents is absolutely unfair.

Oh, come on, do you really think insurance companies don't break out all the different factors? They're in a cutthroat business where every tiny margin they make over their competitors is a big advantage. But if they guess wrong, they'll cut premiums too aggressively and lose money on accident claims.

Car size and gender are both factors. It's possible they're partly correlated, but actuaries can still incorporate both factors into their models.

Charging higher premiums for men is fair if you only care about accident rates. If you feel it's a form of discrimination and want to level the playing field, that has to be done via government regulation.


>The same with men and women - I think it's fair to say that men drive more powerful, larger cars(on average) than women do. So....they get into more accidents because the cars they drive are objectively more difficult to drive. Which obviously means that you should calculate your premium based on the type of vehicle being insured, not on the gender of the driver - and, by extension, insurance for a man and a woman, on the same vehicle, should be identical. Penalizing a man in this situation because men as a group get into more accidents is absolutely unfair.

The simplest "AI" (linear regression) will already factor this out if it has the data about the car driven and gender. Any remaining gender imbalance is attributable either to another factor or it is actually gender related (e.g. increased road rage, high speed driving and risky following behavior due to testosterone might be a stronger accident predictor than too careful driving (not speed matching fast enough when merging on the highway), too slow driving or worse spatial reasoning which are usually correlated with higher estrogen levels.)

In general the AI will just become a better predictor the more data it has available and the more detailed the data is. But an effeminate man or a manly woman might behave more than the opposite gender and thus be treated wrongly. We could probably solve this by tacking on a recent endocrinological report to every application, but this gets quite privacy invasive at some point.

In general if you get insurance you want to be part of a larger risk group to mitigate your effects. Having no insurance just means you have the most fair risk group, which is just yourself.


> The issue here is the phrase "fair share" - why is it a fair share? Because women get into fewer accidents? Cool, what if we get out of the statistical data that black people have more accidents(this is just an example, I don't know if they really do)?

Because actuarial tables say so. Insurance companies that decided it did not matter folded or changed their opinion after the losses


So every insurance company in the EU collapsed after charging people more depending on gender was banned EU-wide?


They leveled the prices at the higher rates.


In the case of life insurance or auto insurance, if charge the same by gender, it basically means subsidization of male by female. Given the female was the more disadvantaged group historically, it felt wrong.

Race is the other way around.


I'll take a swing.

Insurance companies have a strong incentive to price policies accurately, so you have to assume that discriminatory decisions are made with that goal in mind. Any restriction on factors that make the model less accurate (like forbidding race) will lead to higher volatility and a lower stock price (insurance companies are prized in portfolio management for their low volatility).

The government, however, recognizes that most people are forced to buy car insurance, which means that they are being forced to pay for those profit-maximizing decisions. And to the extent that being forced to pay can creates a downward spiral of high costs leading to poor decision making and even higher costs, that is something to be prevented.

So where to draw the line? Well, the people most at risk of getting into a downward spiral are the already economically disadvantaged (read:poor). The government already prevents considering whether the insured is poor (either explicitly or implicitly by not allowing questions about income/net worth on applications).

But really, any factor which would tend to disproportionately target the already-poor would need to be limited as well, since that can often become an end-run around the limitation. If you consider the factors that would do that at the population level, race would be very strong indicator of income, but gender would not be. (yes, I know women make less in the same jobs, but we're talking about the macro here, including women who are heads of household, 2-income families, and stay-at-home moms).

Now, it doesn't explain why other factors that would seem to be associated with low income like zipcode are allowed. But to answer your question about why gender is ok to price discriminate against while race is not, I think the answer is we try not to force poor people to pay more for government-mandated things just because they are poor or otherwise exhibit a feature that would characterize them that way to an uninformed observer.


Because it has not been shown that any indicator of causation of insurance risk due to race is anything other than a correlated symptom of other factors, where many properly peer-reviewed studies do seem to show that women, particularly within certain age and economic groups, are slightly safer drivers by an amount that is not small enough to be random noise in the stats. The effect is general enough for it to be defensible to say it is at least in part due to gender. Of course, other properties such as age, location, and profession, are much more significant factors.

While some studies show that it is true that Mexicans/African-Americans/insert-other-group-here are a higher insurance risk due to crime and to a smaller extent due to accidents, this is not because of their ethnic background but because of other perhaps correlated but not causally linked factors - they are more likely to live in poor areas with higher crime rates and not be able to afford better security measures. Being Mexican/black/other is not a causal factor despite there being some correlation, so it is not fair to discriminate based on that property, any discrimination should be based upon the causal factors, rather than the lazy correlation, to avoid being unfair to those said factors do not apply to.

(FYI: male here, with many an anecdotal tale to support us being worse drivers generally, if only by a little bit)

lt;cbatr: correlation does not imply causation. It is not fair to discriminate based on factors that are merely correlated with the risk, but it is fair to discriminate based on factors that can be shown to be causally related to the insurance risk.

Of course in the UK (and the EU more generally?) it has gone the other way: insurance companies were forced to stop discriminating by gender which means they can no longer give women lower premiums. Did they lower male premiums at the same time to make up the difference, or just bump female premiums up and pocket the extra? Go on, guess...


If there is no basis for being higher risk, why wouldn't an insurance company want to make more money?

Are you suggesting that their "inherent racism" is causing them to forgo shareholder profits?


They need to be as cheap as possible to remain competitive. This means identifying risky clients and either charging them more or worse refusing their business to make sure they are not taking on too much risk while trying to be competitive. If this were not the case then they would keep it simple and just charge a flat rate (that covers the highest risk they are willing to accept) to all customers.

Using discriminating factors that are simply correlated rather than causal is unfair as it penalises some people within a given profiled group for no good reason. This may not be due to inherent racism, but merely due to not properly understanding the statistics. Or not due to inherent racism now but because of lazy "it has always been done this way" reasoning. It may in part be due to racism, of course, as people are less likely to question results that agree with their worldview.

Identifying the correct causal factors and using the as discriminators is fair, and can be demonstrated to be better for the business too by allowing lower prices for some groups aiding competitiveness, but it can be harder work leading to the less effective and less fair option being used - saving effort now at the expense of being fair (and some potential longer-term business benefits).


Something I see come up regularly in discussions about women in tech is that women are often treated differently in their upbringing. For instance, many report an implicit (or even sometimes explicit) "computers are for boys" message that came from their caregivers, which shaped their connection to the tech industry. The causation here is not being a woman, it is being exposed to an environment that treats women in a certain way.

A similar norm I often see from caregivers is that they consider boys to be "rough and tumble" and girls "soft and dainty". It is easy to see how those behaviour classes could extend to insurance risk.

Is being a woman/man really the causation? Or is it something like the environment that the genders are brought up in? If it is the latter, how is that any different than a minority being brought up in a poor neighbourhood (and, statistically, stay there)? After all, in both cases, you are penalizing those who had an upbringing that is outside of the statistical norm due to a presence of correlation.


And young women (girls to me) drive less because young men (boys) tend to drive when they are dating. So maybe it's not because of their gender (or perhaps more properly sex here) but because of other "perhaps correlated but not causally linked factors."

With a lot of states changing the requirements for drivers permits to require more training and disallowing multiple occupants under certain conditions when the driver is newly licensed it will be interesting to see if this changes.


> It is not fair to discriminate based on factors that are merely correlated with the risk, but it is fair to discriminate based on factors that can be shown to be causally related to the insurance risk.

Why should an actuary care about cause? The correlation is what matters for that purpose.


Here's one reason: In most countries people have an official sex, shown on official documents. (That will change one day: sex should be treated as potentially confidential medical data.) However, most countries have never had an official concept of "race". So if an insurer asked people for their race they could answer whatever gives them the lowest premium and they could not be accused of lying.


That's not true. Most countries don't have an official concept of social classes, so that makes it okay to say that you earn above X (e.g. to someone lending you money) even though you're not making anything? It's still lying.


Most countries have an official concept of annual income. It's used to assess income tax.


Most countries also have laws that prohibit the employer from asking for this information, it's still not OK to lie. Refuse to disclose, yes, but don't lie.

Edit: guys, you should probably consult a lawyer. If you don't refuse to disclose but lie instead, that's called deception and at least in the EU it's a criminal offense. Also, morality, huh?


Is deception in general illegal in the EU? I don't think that is the case in the US. Fraud in the US requires that the person being deceived suffers an injury as a result of their reliance on a misrepresentation of a fact. That would be the case if you were applying for a loan because your income affects your ability to repay the loan, but I don't think it would be the case for an employer because they aren't harmed by your income being lower than what you tell them it is.


Yes, deception is illegal even if no one was, is or will be harmed; the rule is that it's illegal if the deception was made for profit (not just monetary). There are many situations where you profit from deception even though no one is harmed, and that's illegal.


What if insurer asks for the applicant's passport photo and then uses artificial or human intelligence to classify the applicant by race?


That's an interesting question. Of course, not everyone has a passport, but that's a nit: people applying for car insurance typically do have a driving licence with a photo. What would an AI make of a huge database of photos and insurance claims? I'd love it if someone would try the experiment! I doubt the AI would invent the concept of "race" (whatever that means), but it might decide that people with tattoos or piercings should pay more, for example.


If the insurers have everyone's photo, the AI can directly correlate the photos to accident rates and discriminate based on whatever mysterious aspect of the image appearance causes the correlation.


> That will change one day: sex should be treated as potentially confidential medical data.

Why “should” it? Don’t hold your breath.


Because differential treatment between the sexes doesn't aggregate and self-amplify negative economic outcomes across generations to create distinct economic disparities between minorities?


Feminists have argued that differential treatment between the sexes has aggregated and self-amplified across generations, to the detriment of women.

Why wouldn't the same be true of differential treatment to the detriment of men?


>Feminists have argued

Do you? Because you're going to actually have to make this argument for me - I don't think I've ever seen feminists say that capital accumulation mechanics are the cause of misogyny.


Not capital accumulation, but social expectations. In this case, one obvious candidate would be that the stereotype that young men are more dangerous drivers creates a social pressure for young men to take more risks to prove their manhood.

In this case auto insurance rates are both a consequence of the stereotype and a mechanism that imposes it on new drivers.


>In this case auto insurance rates are ... a mechanism that imposes [the stereotype] on new drivers.

The argument for restricting discrimination on a gendered basis relies on the socially instructive nature of vehicular insurance to teenagers.

That feels exceptionally weak.


One of many factors. Movies and other fiction are no doubt the most powerful force communicating that expectation that young men should act like race car drivers.


> In fact, young people in general would have to get the same price as everyone else?

I really enjoy the fact that young people pay for car "insurance" more because of their increased risk at driving, but old people don't pay for health "insurance" more (if they pay at all) because of their increased risk at getting ill. Because one of those would be totally unfair while the other one is totally OK. You understand which one is which, right?

It is basically youth serfdom.


> I really enjoy the fact that young people pay for car "insurance" more because of their increased risk at driving, but old people don't pay for health "insurance" more (if they pay at all) because of their increased risk at getting ill

My health plan, purchased through my state's ACA exchange in the US, costs me as a 57 year old 2.71x what it would have cost a 23 year old purchasing the exact same plan.

I would happily take a 23 year old's car insurance premiums if I could get a 23 year old's health insurance premiums in exchange.


> old people don't pay for health "insurance" more

They do in fact pay much more for health insurance. US law does now limit how large of a difference there can be to lower than the natural amount, though, so young people are subsidizing older people's health insurance.


Here in the UK, if you elect to get private health cover in addition to the NHS-provided general care, older people do pay more.


I think they pay more in the US as well? I thought the aspect of ACA limits how much more they have to pay (and the Republican drafts had similar rules, but allowed a greater differential).

The reasoning being, over your lifetime you pay a bit more when young, which acts as a virtual savings account for when your older and need more care.

If you relied on people saving, they wouldn't and you'd have old people dying in the streets. Similar logic applies to enforcing pension savings.


But it's not savings it's redistribution, i.e. extortion.

Young generation is guaranteed nothing for their contribution.

It could only be sound if population is to grow organically and indefinitely, which will obviously be unsustainable either.


The society getting richer is probably the defining property that makes it sustainable, not the number of bodies in it.


Unfortunately that's not how modern economy works. And we don't know how to make it work that way.

Modern economy works in this way: you give up ~40% of your income, which is then redistributed between retirees. Infrastructure and all other kinds of social programs also take their cut. This works when there's two workers for every retired person

If there would be as many retirees as workers, you will end up giving 80% of your income. But it is totally unsustainable. Imagine that you pay $10 for a coffee, and the barista only gets $2 towards his account, post-tax. She won't be able to make decent living. You strangle life out of economy and make it go to grey zone.

Yes we may be able to produce endless amount of wealth, but we are not able to make this sheet balance itself. There's just 100 of percents and they don't make those anymore.


What happens in your case if an entity is not in any way "aware" of people's protected class (they don't collect that info) but using these other properties coincidentally affect a protected class?

Say you _don't_ collect info on people's religiousness but other properties you collect in effect result in you either discriminating for or against either religious people or perhaps against atheists.

If there is no awareness, is there still liability?


Yes, you can still be liable even if you're not aware:

"Where a disparate impact is shown, the plaintiff can prevail without the necessity of showing intentional discrimination unless the defendant employer demonstrates that the practice or policy in question has a demonstrable relationship to the requirements of the job in question"

https://en.wikipedia.org/wiki/Disparate_impact


I'm not a lawyer, but I'd say if you make a good faith effort to prevent that eventuality, it would probably at least mitigate liability. That's a great question, though.


There's a simple technique to "control for" those factors. First you split your population into groups that each have a similar race, gender, and age. Then within those groups you make comparisons of the hotmail users to the others. https://en.wikipedia.org/wiki/Controlling_for_a_variable

You would need to keep in mind xkcd #882 (https://xkcd.com/882/).


Let me ask some clarifying questions, because I think I understand the broad strokes of what you're saying, but I'm not clear on some details.

OK, so I find that among Black users, Hotmail is associated with a 1.05 relative risk to the baseline, and among white users, 1.15 relative risk. How do I then apply this knowledge to an incoming user of an unknown race with a Hotmail account? Do I give them the population weighted average of of the relative risks among all my buckets? What if I do know their race? Do Black users get assigned 1.05 as much risk and white users get 1.15?

Also, what if some of my buckets aren't full enough to get a high confidence of the relative Hotmail risk in that bucket? Let's say I don't have enough gay Hispanic women in my cohort. Do I just drop them from the analysis and hope they're similar to the other populations?


Also, what if Hotmail is associated with a 1.15 relative risk for both black and white users, but a black user is significantly more likely to have a Hotmail account than a white user?




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