Each has a plausible justification for inclusion. But the more data points you have, the better you're able to predict race from them (or anything else you're not supposed to).
So how do you decide if an algorithm is discriminatory? Maybe a whitelist or blacklist of what data it can use?
If you use time in bar to predict cirrhosis it's ok.
etc
We may say that now, but what if later some algorithm finds there's a hidden correlation between driving performance and race/gender? will it still be an ok metric then? where do we draw the line?
The line is between inferences of bad behavior -> bad result such as "you drive risky so you must be at risk of an accident" and pure correlation "you drive risky so you must be a man so you must be at increased risk of prostate cancer".
And conclusions should be assumed to be of the latter kind unless shown otherwise.
It might well be the case that a totally 'colour-blind' scoring algorithm still ends up sorting people by one of those mentioned, unrelated data points. This is not racism, it is just the consequence of the mentioned relationships. Calling it racism is a sign of ignorance and does a disservice to the effort to get rid of true racism.