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I think we can use dangerous data in safe ways. If you use how you drive only to predict likelihood of car accident that's ok. If you use it to predict future wage growth it's not ok. If you use it in a blackbox model it's not ok.

If you use time in bar to predict cirrhosis it's ok.

etc

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>If you use how you drive only to predict likelihood of car accident that's ok.

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?

I said that expecting such a correlation to exist.

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.

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