Consider that not everyone understands how machine learning, and specifically classifier algorithms work. When a police officer is told the confidence level is above 75% he's going to think that's a low chance of being wrong. He does not have the background in math to realize that given a large enough real population size being classified via facial recognition, a 75% confidence level is utterly useless.
The reported 75% confidence level is only valid when scanning a population size that is at most as large as the training data set's. However, we have no way of decreasing that confidence level to be accurate when comparing against the real world population size of an area without simply making the entire real population the training set. And none of that takes circumstances like low light level or lens distortion into account. The real confidence of a match after accounting for those factors would put nearly all real world use cases below 10%.
Now imagine that the same cop you have to explain this to has already been sold this system by people who work in sales and marketing. Any expectation that ALL police officers will correctly assess the systems results and behave accordingly fails to recognize that cops are human, and above all, cops are not mathematicians or data scientists. Perhaps there are processes to give police officers actionable information and training that would normally avoid problems, but all it takes is one cop getting emotional about one possible match for any carefully designed system to fail.
Again, the frequency of cops getting emotional or simply deciding that even a 10% possibility that someone they are about to question might be dangerous is too high a risk, is unlikely to change. So,providing them a system which increases their number of actionable leads and therefore interactions with the public can only increase the number incidents where police end up brutalizing or even killing someone innocent.