Canadian man changes gender on government IDs for cheaper car insurance
cbc.ca
cbc.ca
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.
There seems to be a difference between the different ethnic groups.
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.
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.
The ACA provides a similar legal protection for gender in health insurance. We just need to extend that protection to car insurance.
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 think this hypothesis is consistent with the fact that ACA plan rates depend on your age, which you have no control over.
http://www.msnbc.com/msnbc/canadian-woman-gives-birth-americ...
1) men pay more
or
2) men and women pay the same
?
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.
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.
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.
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
Catch up Canada!
They also use first name to discriminate. Put in "Wayne", and the quotes are much higher than "Sarah".
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.
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.
how about legally recognized "contextual gender", i.e. a woman while driving the car, a man while applying jobs etc...
/s
> "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.
And if so, and more broadly, is there a problem with changing gender identification to gain a preferential price or service?
Repair costs go hand-in-hand with car prices. Add in icy roads for extra fun.
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.
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.
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.
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.
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.
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.
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.
Is is discrimination to require a fitness test be taken for certain jobs/insurance?
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.
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.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3175099/#S12tit...
Having men-only domestic violence shelter hardly requires removing the gender requirements of our current shelters.
> "I did it for cheaper car insurance."
Yeah, I see what you did.
"it's because of the insurance!! I swear!!"
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.
Please explain to me how making an actuarial table with different data sets would get you the same answer without bias.
> 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.
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?
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.
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.
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.
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.