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.

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.