That could be a tricky subject, but the post on insurance and sex discrimination, and particularly some of the replies, led me to ponder how one can rationally decide if this change is right or wrong. I.e. questioning myself (as one should always do).
In any case in making a decision one has to consider the underlying objectives, and how you justify picking those objectives even. Often you can reduce a problem to "obvious" right and wrong objectives.
The insurance one is a tad tricky. Naturally, as a scientist, I considered the factors like statistics, and how "wrong" it is inherently to remove source data (gender) from the equation. It is, of course, right to question if the statistics on gender do in fact create a reliable predictor of future risk for insurers. I am sure they ask themselves this on every factor, including gender, and I assume the answer is yes. But essentially what we are saying is that there is a correlation between gender and risk. As any statistician knows you have to be careful with correlations. If A correlates with B, it could mean A causes B, it could mean B causes A, it could mean C is involved somehow and that neither A causes B or B causes A. It can also be a fluke, but usually there would be some cause. A recent xkcd is a good example. It shows correlations by showing maps of where certain things are common in the country. They line up - they correlate. But do that simply because they are just population maps.
One nice counter example of the use of statistics and discrimination was gievn to me. It was basically "Statistically black people are involved in more crime - therefore employers should be able to use that statistic to decide not to employ black people". A big problem with that is that it can cause a loop. If you don't employ one group of people it is likely they will be more involved in crime, bolstering your original statistic, regardless of the original cause. Before long you have a self justifying policy.
However, when it comes to insurance, a business that is wholly about statistics, it makes sense that any factor which statistically predicts future risk is worth using. It is also very hard to see how this could create a feedback loop. Indeed, it is easy to see how high prices on insurance lead to less driving or driving slower and cheaper to insure cars, so reducing risk in future. A negative feedback that damps the effect.
So, from a purely mathematical point of view, it is obvious that insurers should be able to use any and all factors they can in deciding risk. Any policy has to result in a stable solution which is morally acceptable given the naturally selfish nature of people and companies. It is, obviously, in their commercial interests to get it right, and so to find factors which fail to be good predictors and remove them. So allowing insurers to use any factors they can, including gender, leads to more accurate risk prediction.
Now, is that "right"? Well, to tell that one has to consider whether insurance with more or less accurate risk prediction is "right". Turns out that is a lot harder to come to any conclusion on. One common method is to consider the extreme cases, and see if they seem right or wrong - that can help align your moral compass on such things.
The problem is that it comes down to the age old difference between communism and capitalism!
Lets assume you want more accurate predictions. The premise being that it is "unfair" for careful driver to pay high premiums to help pay for people that have claims. The extreme of this is an insurance company with access to a time machine. That changes insurance. It becomes a pre-payment installment plan to pay for what will happen. "Certainly sir, you can have a 27 year term life insurance policy, but I'm afraid the quote for 28 year term is rather more expensive". Arguably paying for your own claims is "fair", for some definitions of "fair".
At the other extreme you have insurance with no prediction of risk, just a cost to cover. That means everyone paying the same amount for insurance, even people that do not drive (after all pedestrians and passengers benefit too). That is "fair" as everyone pays the same. In some ways this is what we aim for in the NHS.
I can't decide which is better. Obviously, as extremes, they are both a problem. They would both cause feedback loops affecting behaviour. But without knowing which direction is better you cannot say if a change from one status-quo to another is right or wrong.
The change to remove gender is a change in the direction of the "everyone pays the same" end of the scale. If that is, in fact, "right", surely we need more changes towards that end of the scale.
Fun, isn't it?