101–110 of 201 posts
sorry, just checking - young black men are statisically less likely to rape than equivalent white (young men) That is surprising. And given this discussion about only wanting stats that have a valid explanation as well as fitting the facts, is interesting.
Well. At the end of the day, the companies will pick thresholds and rates to maximize their profits, based upon the data that they have available. They don't know every detail of our personal lives -- which would also be kind of unsettling -- so they have to resort to a simplified picture. Simplifications are always prejudiced at the individual level but if the prejudice is reflected in the numbers, it's balanced. It…
Collectivism is just fine when you are the one on the receiving end. Either we set absolute lines of discrimination across all industries or we disallow it entirely.
This is how it should be: "Max Profit. The most profitable, since there are no constraints. But the two groups have different thresholds, meaning they are held to different standards." Here's the big fallacy: "the two groups have different thresholds, meaning they are held to different standards." They are not held to different standards because they're different groups, but because of other reasons that indicate dif…
Any SJW care to explain why I'm wrong instead of downvoting? thanks! this is a lot of fun :D
You realize you're advocating totalitarianism, right?
We detached this subthread from https://news.ycombinator.com/item?id=13005244 and marked it off-topic.
Earlier quoted context omitted.
The difference with machine learning is that the model isn't designed by humans, through an actuarial process we can keep our brains wrapped around. It's a black box. We are OK attributing "crash risk" to young male drivers, because we can observe both that they are as a cohort statistically likely to crash and also understand why that would be the case. On the other hand, we're not comfortable with the idea that a c…
Please don't say "we". Not everyone shares your politics. I'm perfectly happy with algorithms detecting that certain people are more likely to be safe drivers than average, and giving them lower rates, and concentrating premiums on the groups more likely to be in accidents, even if I don't understand why Armenians (in your example) get in more crashes.
Earlier quoted context omitted.
Now, this is his first offence, but a young man is found high on PCB walking down the street hitting cars with a baseball bat. It takes five cops and a significant struggle to arrest him. Having read that, what would you assume is race and economic background is? After criminal proceedings he was sentenced to community service. Now, what would you assume is race and economic background is? PS: Bias is insidious and r…
I wouldn't make assumptions, but I don't understand your point.
However, most people will make assumption on race and class from things like PCP even though zero information about that in my example.
Earlier quoted context omitted.
No, I think he's more advocating for a world where we don't encode the literal status quo into computer models that make decisions for us going forward, which is something that nobody of any political stripe is likely to want.
What's wrong with encoding the status quo? Why do we have to encode progressive extremism into computer models? Being progressive for the sake of "going forward" sounds like misguided idealism. What do we do when progressive computer models lead us down a harmful path, do we simply play the typical liberal blame game and point fingers everywhere else while digging our head into sand?
Earlier quoted context omitted.
The difference with machine learning is that the model isn't designed by humans, through an actuarial process we can keep our brains wrapped around. It's a black box. We are OK attributing "crash risk" to young male drivers, because we can observe both that they are as a cohort statistically likely to crash and also understand why that would be the case. On the other hand, we're not comfortable with the idea that a c…
I don't get the impression that most people are afraid of irrelevant correlations, but rather relevant ones that nevertheless disproportionately affect certain populations. To use your Armenian example, it could be that while being Armenian doesn't actually affect your driving, a "true" model could still end up being bad for Armenians if being Armenian is correlated with the things that actually do affect crash risk.…
I'm just incredibly impressed that you came up with a way to make this sound even worse.. can you imagine the government giving subsidies to Armenian drivers with good records because insurance companies are overcharging them due to the statistical performance of their category?
A tax benefit for being an outlier from the mean? My god. Not only does that sound like a terrible idea, but intensely complicated to organize in the general case.
And what happens when an individual is in multiple categories? What if only gay Armenians who listen to disco are the at-risk driving category? How do we aggregate the tax subsidies, multiplicatively, additively, ..? This sounds like an administrative nightmare, I congratulate you on your evil ingenuity.
Earlier quoted context omitted.
The difference with machine learning is that the model isn't designed by humans, through an actuarial process we can keep our brains wrapped around. It's a black box. We are OK attributing "crash risk" to young male drivers, because we can observe both that they are as a cohort statistically likely to crash and also understand why that would be the case. On the other hand, we're not comfortable with the idea that a c…
I don't get the impression that most people are afraid of irrelevant correlations, but rather relevant ones that nevertheless disproportionately affect certain populations. To use your Armenian example, it could be that while being Armenian doesn't actually affect your driving, a "true" model could still end up being bad for Armenians if being Armenian is correlated with the things that actually do affect crash risk.…
Lets say that I am male, and am young. Lets say that as a young male driver, I am likely to be a cause of an accident 20% of the time i.e. 1 in 5 young male drivers will cause an accident. Lets say as a young female driver, I am likely to be the cause of an accident 5% of the time.
So on the face of it, young male drivers are riskier, and should pay appropriately.
But lets say there was another measurable factor, such as a 'recklessness' score, which describes how recklessly you behave. And lets say that if you are 'reckless', you are likely to be the cause of an accident 90% of the time. And the maths works out that if you take the 'not reckless' group from men and women, they are equally likely to be the cause of an accident, i.e. there are more reckless men than women.
If you were to take the stats at face value, then a good proportion of people are being over and under-charged, because you are using a proxy measure rather than digging deeper for a root cause/don't have the data available. I think this is exactly what people are afraid of, businesses/people being lazy and making assumptions that are correlative, based on characteristics that one can't change.
Earlier quoted context omitted.
Interestingly the EU has banned insurance underwriting based on gender even with all the actuarial data backing it up. Age is still fair game though.
So (assuming males are more costly to insure) either (a) females pay more than they should and are effectively subsidizing males or (b) males pay less than they should and the insurance companies will go broke..
Fortunately I'm sure people are hard at work finding "proxy factors" which happen to map to pretty well gender but have an acceptable parallel explanation for why they're meaningfully descriptive of an applicants risk profile :)