Earlier quoted context omitted.
>He noted that even if it did target the wrong person, "it doesn't mean they're going to get convicted or arrested, it just gives the detective a look at who could be involved." "Hmm, this black person was near..."
Ironic choice of examples, because lacking these tools, you've just identified the tool the police tend to fall back on. We can build algorithms to avoid racial bias, with awareness and effort. How confident are you we can train cops to do the same?
How confident are you that we can build algorithms to do that? You can't just flatten the probability distribution based on race when many of the legitimate factors are correlated with it, any more than you could do so for gender or age or nationality. Unless you want an absurd false positive rate against octogenarian women from Japan.
But given that some racial (or gender or age) disparity is expected, how do you know if the amount of disparity in the algorithm's results is legitimate? It could be too high, or too low, and knowing which one would imply possession of an algorithm that gives the "true" amount, which was the original problem to begin with.
The answer is to require high standards of proof and conclusive evidence, so that it's impossible to choose a random innocent black man off the street and convict him of anything just because you're a racist or some algorithm decided he fit an aggregate profile.
But that's the opposite of the dragnet approach. You need evidence on a specific person, not statistical data from thousands that doesn't give you a better than 5% probability that it was any given one of them. And suppose the actual perpetrator wasn't carrying a phone at the time of the crime, so now you've got a list of "possible suspects" 100% of which are innocent.