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...
Are you suggesting that their "inherent racism" is causing them to forgo shareholder profits?
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).
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.
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.
Why should an actuary care about cause? The correlation is what matters for that purpose.