Canadian man changes gender on government IDs for cheaper car insurance(cbc.ca)
cbc.ca
Canadian man changes gender on government IDs for cheaper car insurance
https://www.cbc.ca/news/canada/calgary/change-gender-identification-insurance-alberta-1.4754416?cmp=rss
120 comments
It's interesting how people generally consider it acceptable for insurance companies to discriminate based on gender, when that discrimination would probably not be considered OK in other areas. You don't choose your gender, why should you be punished for it? Would people also be OK if insurance companies discriminated based on race? Surely there is some correlation between race and collision risk as well.
I know you’re trying to do a reductio ad absurdam here, but insurance companies already do discriminate based on race. In fact, it would be irresponsible for medical insurance providers, in particular, to not discriminate (that is, have different premiums) based on race.
Black people have more heart attacks, white people are more likely to develop Seasonal Affective Disorder in the same geographic region, etc. It’s nothing to do with predicting behaviour, it’s just differing physiologies, causing different levels of risk of certain physiological conditions irrespective of the choices we make or how we’re raised.
As well, different races respond better or worse to different drugs. There might be a cheap way to treat hispanic people for a condition but only an expensive way to treat any other race. In that situation, if there was actuarial data saying that Hispanic person A and non-Hispanic person B had an equal chance of developing that condition, it would make sense for person B’s premiums to be higher, no?
If you can predict, per person, not only the chance of an event causing a condition requiring an insurance-covered response; but also predict, per person, the size of the total insurance pay-out from a given event, then people who will need more expensive treatments for the same condition will need higher premiums. It’s like flood insurance: insuring a house closer to the coast costs more, but insuring a larger house also costs more. Because the pay-out will have to be higher to fix a larger house, even though the flood was the same. Same damage—more expensive solution required.
Black people have more heart attacks, white people are more likely to develop Seasonal Affective Disorder in the same geographic region, etc. It’s nothing to do with predicting behaviour, it’s just differing physiologies, causing different levels of risk of certain physiological conditions irrespective of the choices we make or how we’re raised.
As well, different races respond better or worse to different drugs. There might be a cheap way to treat hispanic people for a condition but only an expensive way to treat any other race. In that situation, if there was actuarial data saying that Hispanic person A and non-Hispanic person B had an equal chance of developing that condition, it would make sense for person B’s premiums to be higher, no?
If you can predict, per person, not only the chance of an event causing a condition requiring an insurance-covered response; but also predict, per person, the size of the total insurance pay-out from a given event, then people who will need more expensive treatments for the same condition will need higher premiums. It’s like flood insurance: insuring a house closer to the coast costs more, but insuring a larger house also costs more. Because the pay-out will have to be higher to fix a larger house, even though the flood was the same. Same damage—more expensive solution required.
In the US, it is illegal to use race (or gender), both of which are highly predictive of health care costs. You can use age and smoking, but that’s about it, at least on individual issuance under ACA. The age increase is about 50% of what the true actuarial cost is, so the young subsidize the old.
The result is a healthy 26mo white man gets overcharged and a pregnant, unhealthy, overweight 40yo black woman is undercharged. Usually this just means the 26yo chooses not to get insurance.
(Men are cheaper because they don’t have as many routine medical exams, they don’t go in to the doctor generally even when they should, they don’t get pregnant and their health problems seem to include sudden out-of-hospital death more than chronic conditions. That makes them substantially cheaper to insure even if not actually “healthier”.)
Pre existing conditions and known upcoming things (like, planning to get pregnant) or superior knowledge of one’s health status and risk profile make health insurance a really defective marketplace.
The result is a healthy 26mo white man gets overcharged and a pregnant, unhealthy, overweight 40yo black woman is undercharged. Usually this just means the 26yo chooses not to get insurance.
(Men are cheaper because they don’t have as many routine medical exams, they don’t go in to the doctor generally even when they should, they don’t get pregnant and their health problems seem to include sudden out-of-hospital death more than chronic conditions. That makes them substantially cheaper to insure even if not actually “healthier”.)
Pre existing conditions and known upcoming things (like, planning to get pregnant) or superior knowledge of one’s health status and risk profile make health insurance a really defective marketplace.
It’s also hard to verify if they are indeed discriminating. Insurance companies stand behind a veil of “complex model”.
I strongly disagree. The US is still highly highly segregated.
Just because it’s illegal doesn’t mean it doesn’t happen. Discrimination in the case of insurance, advertising, housing, etc happens by zip code.
Live in the wrong zip code and you’re screwed.
Just because it’s illegal doesn’t mean it doesn’t happen. Discrimination in the case of insurance, advertising, housing, etc happens by zip code.
Live in the wrong zip code and you’re screwed.
[flagged]
Please keep generic ideological comments off HN.
https://news.ycombinator.com/newsguidelines.html
https://news.ycombinator.com/newsguidelines.html
>Black people have more heart attacks,
Nothing to do with race, more about social standing. Black people tend to be less educated/affluent. Poor diet -> obesity -> heart attacks. 33% of whites are obese, while 48% of blacks are obese. Much more information can be found here: https://stateofobesity.org/disparities/
>white people are more likely to develop Seasonal Affective Disorder in the same geographic region, etc.
Again, nothing to do with race. White people tend to live in places like Alaska, Maine, etc. which have long and cold (and dark) winters.
Nothing to do with race, more about social standing. Black people tend to be less educated/affluent. Poor diet -> obesity -> heart attacks. 33% of whites are obese, while 48% of blacks are obese. Much more information can be found here: https://stateofobesity.org/disparities/
>white people are more likely to develop Seasonal Affective Disorder in the same geographic region, etc.
Again, nothing to do with race. White people tend to live in places like Alaska, Maine, etc. which have long and cold (and dark) winters.
I don't know if he's right or wrong but he wrote white people are more likely to develop Seasonal Affective Disorder in the same geographic region", so saying that white people tend to live in Alaska or Maine doesn't explain anything.
Insurers are legally forbidden from using race directly. Two people of different races who list identical details on their application should receive identical quotes.
The ACA provides a similar legal protection for gender in health insurance. We just need to extend that protection to car insurance.
The ACA provides a similar legal protection for gender in health insurance. We just need to extend that protection to car insurance.
Wow, I had no idea they discriminated based on race, that sounds kind of wrong to me. I mean for medical purposes it makes sense, do they also do it for auto insurance, where, in theory, your race does not make a difference?
They don't discriminate based on race. They do discriminate based on ZIP code, which is correlated with race.
https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/... page #16 shows deaths by "Ethnicity" and "gender."
There seems to be a difference between the different ethnic groups.
There seems to be a difference between the different ethnic groups.
It seems that each time a discrimination is in favor of women, there is no debate about it. It’s a strong hint that the self-named gender equality movement is not what it pretends it is.
Yeah this is totally the fault of feminists.
Usually its considered OK to discriminate against a historically privileged class...
It’s a tough philosophical question as to whether it is fair to discriminate based on gender to determine insurance rates. In the U.S. we split the difference. For car insurance and life insurance, where men have significantly greater risk, men pay more, because they cost the insurance company more. For health insurance, where women cost the insurance company more, insurance companies are forced to charge the same rates, because it’s wrong to make someone pay more for something based on an inherent trait they have no control over.
That doesn't seem like splitting the difference as much as putting the costs on men...
Obviously you never tried getting private insurance that covers pregnancy pre-ACA. Always a separate rider, always expensive. Whoopsie if you didn't catch that fact, failed to get a separate rider, then went on to have pregnancy complication. Hello bankruptcy! Hell, even with the pregnancy rider, a complicated pregnancy put a lot of insured people into the poorhouse.
Pregnancy complication example #1. Even with insurance, pregnancy can be treated as a preexisting condition and denied coverage. Wonder if her plan excluded the rider?
http://www.msnbc.com/msnbc/canadian-woman-gives-birth-americ...
http://www.msnbc.com/msnbc/canadian-woman-gives-birth-americ...
"For health insurance, where women cost the insurance company more, insurance companies are forced to charge the same rates, because it’s wrong to make someone pay more for something based on an inherent trait they have no control over."
I don't think this hypothesis is consistent with the fact that ACA plan rates depend on your age, which you have no control over.
I don't think this hypothesis is consistent with the fact that ACA plan rates depend on your age, which you have no control over.
They split the baby on that one too. Old people can only be charged 3x as much as young people, but the actuarial difference between an 18 year old and a 65 year old is something like 10x, which used to be the price difference too.
How is it splitting the difference when the choices are
1) men pay more
or
2) men and women pay the same
?
1) men pay more
or
2) men and women pay the same
?
I wonder... is there any good solution to this issue from either side? Outside of legitimate gender change (bear with me -- I know some of you don't think this is possible but lets go with the assumption) can you really blame the guy? And as an insurance company what can you do about it? I would say maybe we could have flat rates but then that just incentivizes the "good" class to go to an insurer who values their "goodness".
As a society you can make it illegal to discriminate on factors that a person has no control over.
Per the article the EU already banned gender discrimination for insurance rates in 2011. Catch up Canada!
I wonder why it is that car insurance charges higher rates to young males(who on average cost more than young females), but medical insurance as far as I can tell is equally priced for young men and young women, even though young women generally have higher health care costs than young men(primarily through childbirth and greater use of health services).
I don't want to be cynical but I imagine if a health care company in the US or Canada actually did start charging more for some class of young women vs young men, it would make international news and lead to new anti-discrimination laws for health insurance
I wonder why it is that car insurance charges higher rates to young males(who on average cost more than young females), but medical insurance as far as I can tell is equally priced for young men and young women, even though young women generally have higher health care costs than young men(primarily through childbirth and greater use of health services).
I don't want to be cynical but I imagine if a health care company in the US or Canada actually did start charging more for some class of young women vs young men, it would make international news and lead to new anti-discrimination laws for health insurance
I noticed the lack of gender having an effect on price... However they are still discriminate on "Title" (Mr, Miss, Mrs). It makes a pretty substantial change to quotes I get.
They also use first name to discriminate. Put in "Wayne", and the quotes are much higher than "Sarah".
They also use first name to discriminate. Put in "Wayne", and the quotes are much higher than "Sarah".
[deleted]
Catch up Canada!
It won't harm the insurance companies, as it'll be a level playing field, and they'll all have to simply quote new rates that work out for them given the new data they're allowed to consider. The parties harmed will be, on average, women, whose rates will go up, and the beneficiaries will be, on average, men, especially young men, whose rates will come down.
Speak for yourself, I'm Canadian and would love to eliminate gender discrimination from auto insurance
Oh, good point. Let's also recognize that blacks are more likely to commit violent crimes and have violent crimes committed upon them, so we should be charging them extra for health insurance relative to whites.
BTW in the EU it’s illegal, since a EU court sentence of 2012, to use sex or gender as a rating factor in insurance.
Is paying different amounts based on measured risk factor really discrimination in the way you intend it?
Yes it is. Should the cost of car insurance, health insurance, or life insurance vary based on the measured risk from your skin colour, or height for example?
A “lucky” individual has reduced premiums, while an “unlucky” individual has increased premiums, and neither has any control over it! Comparatively, someone benefits and someone suffers (both are undeserved).
After thinking about it, I want to hear from someone who thinks the opposite to me - that it is perfectly fair for someone to pay more and, thus, someone else to pay less, due to a factor that neither has any choice about.
A “lucky” individual has reduced premiums, while an “unlucky” individual has increased premiums, and neither has any control over it! Comparatively, someone benefits and someone suffers (both are undeserved).
After thinking about it, I want to hear from someone who thinks the opposite to me - that it is perfectly fair for someone to pay more and, thus, someone else to pay less, due to a factor that neither has any choice about.
The reason someone would say it is fair is because insurance isn't designed to just be free healthcare our car repairs or whatever. Insurance is a scene designed to deal with the problem that some unlikely events are too expensive to pay for oneself. We pool our money into insurance policies to allow anyone holding the policy to weather such events by the virtue of having guaranteed support from the other policy-holders.
However, for this system to work, each person does need to pay in approximately enough to cover the risk they create to the policy. So yes, more risky people do need to pay more, because they are creating more costs to the policy.
Basically, insurance isn't free healthcare, and we shouldn't be treating it that way.
However, for this system to work, each person does need to pay in approximately enough to cover the risk they create to the policy. So yes, more risky people do need to pay more, because they are creating more costs to the policy.
Basically, insurance isn't free healthcare, and we shouldn't be treating it that way.
Yes: see redlining, racial profiling, etc. All of these can be "justified" by appealing to measured risk, but we consider them very discriminatory from both an ethical and a legal perspective.
Think about it, though - what is really being measured? Maybe there's a small subset of super high-risk male drivers who the rest of us are subsidizing. If they were to base my insurance rates on measurable individual characteristics, that would be much less suspect IMO.
Think about it, though - what is really being measured? Maybe there's a small subset of super high-risk male drivers who the rest of us are subsidizing. If they were to base my insurance rates on measurable individual characteristics, that would be much less suspect IMO.
Yes, because the risk factor isn't that you're a young male, it is because you are a dangerous driver, which is correlated with young males. But it also means that there are safe driving young men out there who are subsidizing dangerous female drivers.
So, like, salary? After all, you can't control your intelligence...
You raise a thought-provoking point. For example, a company that basis your salary on aptitude tests (or genetic tests - hello Gattica!). It isn’t fair if you didn’t have control over your “intelligence genes” so to say. But, this is very fuzzy right? Your intelligence and aptitude for a job is determined by so many factors, and any tests would struggle to capture the information properly, I think.
By comparison, the car insurance test of gender was straightforward (just look at the letter on the document!). I would hesitate to apply my original statement to such a fuzzy situation.
By comparison, the car insurance test of gender was straightforward (just look at the letter on the document!). I would hesitate to apply my original statement to such a fuzzy situation.
Yeah I don't have a good answer either. I tend to take a more pragmatic approach, as opposed to a moral one, asking myself, what behaviour do we want to encourage? "Discriminating" based on intelligence => meritocracy => probably good. Young men subsidising pregnant women => possibly good (for Western nations at least). What does discriminating of car insurance do - no idea, possibly discourages men from driving and driving recklessly, but the effect is probably minimal and might not even be there, so...
Let's turn this around, people have characteristics that they don't choose and can't change, but those characteristics have an impact, on average, on some real-life aspects. Would ignoring that be a net gain over whatever would be gained by not ignoring it?
BRB, making it illegal to discriminate between dates based on physical appearance.
British Columbia will soon support answering “not specified” for gender on identity documents. I wonder how insurance providers will cope with that. Will they demand you tell them a gender? Will they try to infer it? Will they use low-statistical-power actuarial data for the insurance usage rates of “not-specified people”?
They’ll just buy your demographic profile from a broker for a pittance and use that.
Change it to "Sex" or add have both "Sex" and "Gender" fields.
Let's see if he'll keep the identification when he next applies for a job or a home loan?
Why not?
IIRC, in Canada, its illegal to ask those questions during the application.
Then, when HR is doing their diversity calculations, he boosts their employed woman trans numbers.
Don't see him loosing out much.
IIRC, in Canada, its illegal to ask those questions during the application.
Then, when HR is doing their diversity calculations, he boosts their employed woman trans numbers.
Don't see him loosing out much.
"A crossdresser can dress like a woman in the weekends, and dress like a man during the week. Who is the goverment to tell me to keep the same sex all the time?!!!1"
how about legally recognized "contextual gender", i.e. a woman while driving the car, a man while applying jobs etc...
/s
how about legally recognized "contextual gender", i.e. a woman while driving the car, a man while applying jobs etc...
/s
A male pretending to be a female has an edge over males,females and females pretending to be male when applying for jobs.. Atleast in an unscientific study by
interviewing.io
Depends on where the application goes to. Many companies want to appear politically progressive.
So the simple solution to this type of manipulation is adding a part to the gender change request that says "I swear under threat of legal penalty that I identify as [gender]". Doesn't hurt trans people and makes abuses of the system like this clear cases of legal fraud.
How would you be able to tell the difference between actual personal identification and a lie?
Here in Argentina a man from Salta changed his gender to retire early.
Favorite quote:
> "If you're going to declare on any document, you need to be truthful," he said. "If not, you're making a fraudulent claim. This could impact you for any future insurance application that you make, or any other aspect of your life."
Unless the insurance commissioner is going to provide an objective definition of gender that can be externally and independently verified, I'm afraid that he really has no grounds on which to claim David is a man, rather than a gender-nonconforming woman who prefers male pronouns.
> "If you're going to declare on any document, you need to be truthful," he said. "If not, you're making a fraudulent claim. This could impact you for any future insurance application that you make, or any other aspect of your life."
Unless the insurance commissioner is going to provide an objective definition of gender that can be externally and independently verified, I'm afraid that he really has no grounds on which to claim David is a man, rather than a gender-nonconforming woman who prefers male pronouns.
Is it gender discrimination to charge one gender more than another for the same service?
And if so, and more broadly, is there a problem with changing gender identification to gain a preferential price or service?
And if so, and more broadly, is there a problem with changing gender identification to gain a preferential price or service?
What if the insurance company used machine learning to calculate the premium, which resulted in correlations with gender, race, etc.? Is that also considered discrimination?Whose fault is that?
You would have to show that gender/race were not inputs to the machine learning algorithm. Correlations with inputs that were not restricted would not be a problem (in many situations ZIP code is "close enough" for race.)
The amount of attention and resources spent on "genderism" in the western world is really astounding and accelerating. One has to ask why?
[deleted]
In the Netherlands we had a similar thing. An car insurance marketed specifically to woman, with lower rates, nice pink website and you even got a free purse as a welcome gift. But due to anti-discrimination laws you could simply apply as a man as well, if you could stand the pink website. Of course you got the free purse as well.
Well we've all thought of it now someone's done it (well lots of people have probably done it, this guy is just the first one to risk telling people he's done it). $91/mo adds up.
An man in Argentina changed legal gender so he can retire earlier :
http://www.dailymail.co.uk/news/article-5544173/Argentinian-...
how is it okay that a woman can retire earlier? Men die earlier than women as it is, so this law effectively doubles the amount of time women can enjoy retirement..
How the could car insurance cost $4500 a year? What are you doing in Canada?
https://globalnews.ca/news/2447804/metro-vancouver-the-luxur...
Repair costs go hand-in-hand with car prices. Add in icy roads for extra fun.
Repair costs go hand-in-hand with car prices. Add in icy roads for extra fun.
The icing on the cake is that he's not even in an area known for expensive insurance (Toronto)
Minimum coverage is fairly comprehensive, and they love to charge the hell out of younger folks.
If a car loan is involved, maximum insurance might be required as a condition. Combining that with young male driver, you get a very large number. Also, Canadian dollars, not USD.
It's a $30,000 car and the insurance is probably comprehensive. He could save money by only buying 3rdparty liability (the legally required coverage). If you could afford to replace the car, that's usually the better option.
The problem with just having liability is that it greatly reduces the responsibility of others to repair or replace your car if they hit it. Depending on where you live, you might only get a few hundred dollars from them if they total your car.
You may be willing to pay out of pocket if you crash your $30,000 car into a tree, but you'll probably want someone else to pay for it if they run into it while it's parked.
You may be willing to pay out of pocket if you crash your $30,000 car into a tree, but you'll probably want someone else to pay for it if they run into it while it's parked.
He's in Alberta, which has privately run competition in auto liability insurance. It's BC where ICBC has a government-mandated monopoly on the liability insurance. You can only buy private comprehensive in BC.
Accidents and multiple tickets crank it way up.
Next step: Olympic gold!
Gender identity is one thing, power to you, but at the point we start to let males compete in females sport we've lost our minds.
How do we make it fair? Is it sufficient to lower T to get rid of the advantage? What about females who naturally have higher T levels?
What if, shockingly /s, bone density and height are relevant as an advantage in some sports?
God, I hate even thinking about it.
Transgender rights > Female rights, with the harm going to female athletes. How unsurprisingly sexist.
How do we make it fair? Is it sufficient to lower T to get rid of the advantage? What about females who naturally have higher T levels?
What if, shockingly /s, bone density and height are relevant as an advantage in some sports?
God, I hate even thinking about it.
Transgender rights > Female rights, with the harm going to female athletes. How unsurprisingly sexist.
The world record women's 800m time is surpassed regularly by high school boys.
We already have that problem with genetics interfering with sports. Height has a strong correlation in basketball, and blood levels with endurance sport. Weight and height are used to limit unfair competition in horse racing and boxing.
So one way to keep our minds and still eliminating segregated women and men sport is to segregate based on other attributes that correlate to an unfair advantage, just like those examples above.
So one way to keep our minds and still eliminating segregated women and men sport is to segregate based on other attributes that correlate to an unfair advantage, just like those examples above.
I'm cool with that as well. Happy as a clam if we want to start making a bunch of sport distinctions around advantages such as height, T, bone density, etc.
However, until we do so, the problem remains that there are clearly advantages physically to being a man and that is why we have male and female sporting events. That distinction doesn't magically go away because someone does not identify with their gender. The division isn't about gender, it's about sex.
However, until we do so, the problem remains that there are clearly advantages physically to being a man and that is why we have male and female sporting events. That distinction doesn't magically go away because someone does not identify with their gender. The division isn't about gender, it's about sex.
While we are in an agreement on optimal solution, I don't see why we need to wait. We still have NBL running, and the average hight for men there is about 1 foot (30cm) more. Similar, there were a study rather recently that looked at winter OS winners and found that about 80% of them had blood levels that represented (if I get the zeros right) 1/1000000 chance of naturally occurring. OS is not being put on hold.
Those problems have remained for a very long time and yet we have done very little to remove those clear unfair advantages. What should we do until we fix them? I prefer common solution to similar issues so what ever fix that is deemed acceptable should be usable for all form of genetic advantages, and it would likely resolve a bunch of doping problems at the same time.
Those problems have remained for a very long time and yet we have done very little to remove those clear unfair advantages. What should we do until we fix them? I prefer common solution to similar issues so what ever fix that is deemed acceptable should be usable for all form of genetic advantages, and it would likely resolve a bunch of doping problems at the same time.
I am actually a fan from Serena, but could not hide the fact that she might have a slight advantage....
That woman is an Amazon, and I love her for it. I'm happy to celebrate a female that is uniquely physically excellent in terms of strength, coordination, etc.
There's hardly anything unique about a born male being physically more capable than a born female, however, and I have no interest in celebrating it within the context of female sporting events.
There's hardly anything unique about a born male being physically more capable than a born female, however, and I have no interest in celebrating it within the context of female sporting events.
I feel like that would be mocking.
This is pretty great though. Is it a bit of an abuse of the system, sure. But honestly, if he's being charged more for the same record just because he's a man then I approve of him doing this.
Course I'm a bit annoyed with insurance right now. I made a mistake about 1.5 years ago and was speeding. It was empty highway and I made the stupid decision to go too fast. Got caught.
This is, I paid my fine. I have no accidents, no other tickets. I'm still stuck paying massively increased premiums for years.
This is pretty great though. Is it a bit of an abuse of the system, sure. But honestly, if he's being charged more for the same record just because he's a man then I approve of him doing this.
Course I'm a bit annoyed with insurance right now. I made a mistake about 1.5 years ago and was speeding. It was empty highway and I made the stupid decision to go too fast. Got caught.
This is, I paid my fine. I have no accidents, no other tickets. I'm still stuck paying massively increased premiums for years.
ythn(1)
.
How about non-unisex toilets and women-only domestic violence shelters? Personally, I oppose them, but it appears that most people support them, "discrimination" not-withstanding...
"women-only domestic violence shelters" what exactly do you oppose? That there are no equivalent men's shelters, or that men aren't allowed in?
To this day my wife refuses to tell any man the location of her shelter from when her ex abused her. Might not fit into the modern gender zeitgeist, but I fully support her.
To this day my wife refuses to tell any man the location of her shelter from when her ex abused her. Might not fit into the modern gender zeitgeist, but I fully support her.
Domestic violence agencies could certainly abuse male victims less. A study of male domestic violence victims who contacted local domestic violence agencies for help found that 40.2% were accused of being the batterer by the person "helping" them:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3175099/#S12tit...
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3175099/#S12tit...
Define "discrimination"
Is is discrimination to require a fitness test be taken for certain jobs/insurance?
Is is discrimination to require a fitness test be taken for certain jobs/insurance?
Discriminating based on fitness instead of gender is exactly the sort of thing one should do, if fitness is really what you are worried about.
That way, the occasional woman who is if fit enough can still do the thing that needs doing, and flipping genders on your birth cert doesn't game the system.
That way, the occasional woman who is if fit enough can still do the thing that needs doing, and flipping genders on your birth cert doesn't game the system.
> "I'm a man, 100 per cent. Legally, I'm a woman," he said.
> "I did it for cheaper car insurance."
Yeah, I see what you did.
"it's because of the insurance!! I swear!!"
> "I did it for cheaper car insurance."
Yeah, I see what you did.
"it's because of the insurance!! I swear!!"
This is a perfect example of "AI gone wrong" even though there was no AI involved.
The costs of insurance are based on actuarial tables, which are really just calculations based on large chunks of historical data, much like an AI. And much like an AI, the result essentially magnifies the biases that already exist in the data (biases that may be accurate or may not be).
The tables, nor the AI, care about ethics or perception. They are simply the result of the inputs given.
Do men really have more tickets and accidents? Maybe. Or maybe they just get caught more.
It just highlights how careful we have to be about biases, real or accidental, as we rely more and more on mathematical models based on data.
The costs of insurance are based on actuarial tables, which are really just calculations based on large chunks of historical data, much like an AI. And much like an AI, the result essentially magnifies the biases that already exist in the data (biases that may be accurate or may not be).
The tables, nor the AI, care about ethics or perception. They are simply the result of the inputs given.
Do men really have more tickets and accidents? Maybe. Or maybe they just get caught more.
It just highlights how careful we have to be about biases, real or accidental, as we rely more and more on mathematical models based on data.
Men measurably impact the bottom line of insurance companies more. Actuarial math is not 'AI', there's no 'magnification of bias'. The whole point of the calculation is to eliminate bias so that the company makes sound decisions. Please don't generalize things that you haven't made an effort to understand. When you say stuff like this, you're making it harder to fight actual bias in the world, because you make people not take your point seriously.
I have a pretty deep understanding actually.
Please explain to me how making an actuarial table with different data sets would get you the same answer without bias.
Please explain to me how making an actuarial table with different data sets would get you the same answer without bias.
Bias, according to google:
> prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair.
Data can't be bias. It can be a misrepresentation or inaccurate, but not bias. If I have an accurate data-set that say ethnic group X in area Y has a higher rate of violence, and because of that I make the choice to charge higher premiums for group X- is that bias? No.
You can't scream bias when you don't like the conclusions the data draws. Drawing conclusions from data isn't bias-ed(?) but using only that data might be.
> prejudice in favor of or against one thing, person, or group compared with another, usually in a way considered to be unfair.
Data can't be bias. It can be a misrepresentation or inaccurate, but not bias. If I have an accurate data-set that say ethnic group X in area Y has a higher rate of violence, and because of that I make the choice to charge higher premiums for group X- is that bias? No.
You can't scream bias when you don't like the conclusions the data draws. Drawing conclusions from data isn't bias-ed(?) but using only that data might be.
Technically data can be biased because what you call a misrepresentation is termed bias:
https://en.wikipedia.org/wiki/Sampling_bias
https://en.wikipedia.org/wiki/Selection_bias
https://en.wikipedia.org/wiki/Sampling_bias
https://en.wikipedia.org/wiki/Selection_bias
Don't forget treatment bias, also known as "nature versus nurture". One can totally remove sampling and selection bias by tallying the whole population in the measurement, say in antiquity. I predict the data (which can't have sampling and selection bias) would describe how slaves have a higher injury and mortality rate: after whipping or overworking or being fed inferior food, the injured are last in line when the food is dispensed etc. This is real world treatment bias. How just is it to increase say insurance rate for the slaves? (I know they probably didn't have insurance in antiquity...)
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In other words, statistics done correctly (i.e. representatively) on the real world can tell us what the real world (and its status quo) looks like, but tells us nothing about the ethics of the situation.
Edit:
In other words, statistics done correctly (i.e. representatively) on the real world can tell us what the real world (and its status quo) looks like, but tells us nothing about the ethics of the situation.
True. Inaccurate data can be bias, but I think made that distinction. If you have a representative, statically accurate data set, you can't call the data bias if it points one way or another.
So if I gave you a dataset that only had women in it, and from that you concluded that men never have accidents and should therefore have a $0 insurance rate, you wouldn't say that the data was biased against women?
I would say the data is inaccurate.
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Given that it is impossible to have a 100% accurate dataset for real world events, wouldn't every dataset be inaccurate to some measure? And wouldn't the level of inaccuracy represent a bias of some sort?
There is a whole field of mathematics on how to figure this out.
https://en.wikipedia.org/wiki/Sample_size_determination
And yes, nothing is 100% accurate. The census isn't 100% accurate, but it's good enough. A 95% accuracy is the generally accepted target, which is what you have a statically significant. If you have a target population of 330 Million people, you only need 40k people to hit a 95% confidence value with a 0.1 confidence interval. Again, this has all been figured out, formulated, and settled.
You keep using the word bias and I don't think you know what it means. If the data is inaccurate, it's inaccurate. If I wanted to sample the US population, but only used people from Long Island and only polled 10 people, that's not a bias dataset- it's inaccurate. Conclusions drawn from inaccurate data are inaccurate.
Bias data would be statistics cherry-picked to showcase one view point over another. The dataset itself isn't bias, but maybe the presentation is. As I said before, good (and this is important) data can't be bias- what you do with it can be. Saying "Men are more likely to be bad drivers" is a factual statement. "All Men are bad drivers" is a bias one. See the difference?
https://en.wikipedia.org/wiki/Sample_size_determination
And yes, nothing is 100% accurate. The census isn't 100% accurate, but it's good enough. A 95% accuracy is the generally accepted target, which is what you have a statically significant. If you have a target population of 330 Million people, you only need 40k people to hit a 95% confidence value with a 0.1 confidence interval. Again, this has all been figured out, formulated, and settled.
You keep using the word bias and I don't think you know what it means. If the data is inaccurate, it's inaccurate. If I wanted to sample the US population, but only used people from Long Island and only polled 10 people, that's not a bias dataset- it's inaccurate. Conclusions drawn from inaccurate data are inaccurate.
Bias data would be statistics cherry-picked to showcase one view point over another. The dataset itself isn't bias, but maybe the presentation is. As I said before, good (and this is important) data can't be bias- what you do with it can be. Saying "Men are more likely to be bad drivers" is a factual statement. "All Men are bad drivers" is a bias one. See the difference?
You said: Saying "Men are more likely to be bad drivers" is a factual statement. "All Men are bad drivers" is a bias one.
Let's change it up a little. Take the statement "Black men are more likely to be criminals than white men". Is that factually true? A dataset with a statistically significant sample would say yes, in fact black men are incarcerated at a higher rate per capita than white men.
But look how that data came to be -- by police making arrests. And there is also plenty of data out there showing that black men get arrested more often than white men for the same crimes, and get longer sentences for the same crimes than white men.
So clearly, the dataset that shows black men are more likely to be criminals is biased, even though it is accurate.
Now imagine building an AI (or actuarial table) based on that data. It would necessarily identify black men as "more likely to be a criminal" simply based on the fact that they are black. So now you've magnified the bias in the data.
So yes, the data is accurate but that doesn't mean it isn't biased too.
Let's change it up a little. Take the statement "Black men are more likely to be criminals than white men". Is that factually true? A dataset with a statistically significant sample would say yes, in fact black men are incarcerated at a higher rate per capita than white men.
But look how that data came to be -- by police making arrests. And there is also plenty of data out there showing that black men get arrested more often than white men for the same crimes, and get longer sentences for the same crimes than white men.
So clearly, the dataset that shows black men are more likely to be criminals is biased, even though it is accurate.
Now imagine building an AI (or actuarial table) based on that data. It would necessarily identify black men as "more likely to be a criminal" simply based on the fact that they are black. So now you've magnified the bias in the data.
So yes, the data is accurate but that doesn't mean it isn't biased too.
> But look how that data came to be -- by police making arrests.
Yes. And rape statics come from people getting rapped. I'm going to assume you are talking about convictions here, as arrests and convictions are different.
> And there is also plenty of data out there showing that black men get arrested more often than white men for the same crimes
Ok? Then add that as another data set to your model. You will also need to add in crime relative to the population. Ethnicity of that area. et cetera. You can't just make this statement, say it affects your conclusion without adding in everything else.
This fact doesn't change the initial statement that "Black men are more likely to be criminals than white men." It exposes issues in the society we live in, not the data that is being reflected. Any data scientist worth his salt would be able to take this into account.
White Population of the US is 62%. Black Population in the US is 12.6%. White arrests in the US were 70%, Black arrests were 27%. Whites are over represented by 8%. Blacks by 14.4%. Asians are under represented by 4%. FYI.
https://en.wikipedia.org/wiki/Demography_of_the_United_State... https://ucr.fbi.gov/crime-in-the-u.s/2016/crime-in-the-u.s.-...
> and get longer sentences for the same crimes than white men.
This is irreverent to your point. Ignoring.
> Now imagine building an AI (or actuarial table) based on that data. It would necessarily identify black men as "more likely to be a criminal" simply based on the fact that they are black.
A Machine Learning model, not AI (as AI is incorrectly used everywhere), would come to that conclusion- yes. The problem with your argument is that you want to bring your bias into a system that needs to be based on facts. Do black men get shafted? Yes. But approaching the problem at the end of the pipeline doesn't solve anything.
Going back to the initial discussion point on insurance rates. Men are more likely to die behind the wheel. Ok. We don't fix the problem by saying the data is bias, wrong, whatever. We fix it the source. You're entire argument boils down to "I'm not comfortable with what the data is telling me and I want the data to say something else." Which is emotional- I get that.
> So now you've magnified the bias in the data.
You've built a system that reflects the realities of the world around you. There was an article somewhere about a robbery on a BART train. The police wouldn't release the ethnicity of the suspects for some stupid reason about not wanting to feed into stereotypes. That does nothing to fix the problem, it just tries to cover it up. But, your reaction is the right one you just are not focusing it correctly. Instead of asking why our models look like this, and trying to manipulate them to make you feel better you need to ask why the data is happening the way it is.
My point is this. The conclusion of "Black men are more likely to be criminals than white men" should make you want to fix the reason why, not try and manipulate the data or system that produces that result. If you have a Machine Learning Model that draws that conclusion, you have two options:
1. Fix the source of the data. Isolate why Blacks in the US are over represented in the criminal population by 15%, and solve that.
2. Add in other data sources to further refine your model.
The fix is not run around screaming bias, as that's an incorrect characterization of the situation and actively hurts your cause.
Yes. And rape statics come from people getting rapped. I'm going to assume you are talking about convictions here, as arrests and convictions are different.
> And there is also plenty of data out there showing that black men get arrested more often than white men for the same crimes
Ok? Then add that as another data set to your model. You will also need to add in crime relative to the population. Ethnicity of that area. et cetera. You can't just make this statement, say it affects your conclusion without adding in everything else.
This fact doesn't change the initial statement that "Black men are more likely to be criminals than white men." It exposes issues in the society we live in, not the data that is being reflected. Any data scientist worth his salt would be able to take this into account.
White Population of the US is 62%. Black Population in the US is 12.6%. White arrests in the US were 70%, Black arrests were 27%. Whites are over represented by 8%. Blacks by 14.4%. Asians are under represented by 4%. FYI.
https://en.wikipedia.org/wiki/Demography_of_the_United_State... https://ucr.fbi.gov/crime-in-the-u.s/2016/crime-in-the-u.s.-...
> and get longer sentences for the same crimes than white men.
This is irreverent to your point. Ignoring.
> Now imagine building an AI (or actuarial table) based on that data. It would necessarily identify black men as "more likely to be a criminal" simply based on the fact that they are black.
A Machine Learning model, not AI (as AI is incorrectly used everywhere), would come to that conclusion- yes. The problem with your argument is that you want to bring your bias into a system that needs to be based on facts. Do black men get shafted? Yes. But approaching the problem at the end of the pipeline doesn't solve anything.
Going back to the initial discussion point on insurance rates. Men are more likely to die behind the wheel. Ok. We don't fix the problem by saying the data is bias, wrong, whatever. We fix it the source. You're entire argument boils down to "I'm not comfortable with what the data is telling me and I want the data to say something else." Which is emotional- I get that.
> So now you've magnified the bias in the data.
You've built a system that reflects the realities of the world around you. There was an article somewhere about a robbery on a BART train. The police wouldn't release the ethnicity of the suspects for some stupid reason about not wanting to feed into stereotypes. That does nothing to fix the problem, it just tries to cover it up. But, your reaction is the right one you just are not focusing it correctly. Instead of asking why our models look like this, and trying to manipulate them to make you feel better you need to ask why the data is happening the way it is.
My point is this. The conclusion of "Black men are more likely to be criminals than white men" should make you want to fix the reason why, not try and manipulate the data or system that produces that result. If you have a Machine Learning Model that draws that conclusion, you have two options:
1. Fix the source of the data. Isolate why Blacks in the US are over represented in the criminal population by 15%, and solve that.
2. Add in other data sources to further refine your model.
The fix is not run around screaming bias, as that's an incorrect characterization of the situation and actively hurts your cause.
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> Do men really have more tickets and accidents? Maybe. Or maybe they just get caught more.
The internet seems to indicate that men do cause more accidents, but they also drive more on average. Insurers would only be interested in the likelihood of a payout, so they should rationally charge men more. Also, men get far more DUIs (big payout).
* NOTE: not a rigorous investigation of the numbers.
The internet seems to indicate that men do cause more accidents, but they also drive more on average. Insurers would only be interested in the likelihood of a payout, so they should rationally charge men more. Also, men get far more DUIs (big payout).
* NOTE: not a rigorous investigation of the numbers.
Women get pregnant more than men, but get charged the same medical insurance rates. Women take maternity leave more than men, but legally you cannot pay women less. How come in scenarios where women cost more, it is mandated that men and women must pay the same, but not in cases where men cost more?
Women may get pregnant more, but iirc men have higher mortality rates at all stages of life - I'd also guess way more accidents from risky behavior work related or otherwise. Women also earn less money, and most consumer price discrimination seems to target them (pink shirts cost more than blue shirts) so maybe it sorta works out?
This has nothing to do with AI. Men have been charged more for car insurance (at least in America) for many years. I mean, when my brother got his license almost 15 years ago, he was charged more, and so was I when I got mine.
Yes I know, that's why I said "even though it has nothing to do with AI".
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