There are several issues, though.

1. As discussed in the article, due to training sets or methodology, these systems are poor at distinguishing non-Caucasian faces. Or at least poor relative to a human looking at the image and the person together.

2. Police (though not just them) have an unfounded trust in the machines.

3. Police take this unfounded trust and push through arrests before they realistically should (like in the opening story of the article).

4. People arrested for felonies are fucked in the US. It can take years to get your record expunged. Until then your name shows up in lots of searches, you can lose licenses to conduct some kinds of business (or be unable to obtain them). (Expunging can also be very expensive.)

You may not mind innocent people being caught up in the dragnet, but as a society we probably should care a lot more than we do.

Yeah, those issues may be big problems. Or they may not.

My point is that the way to know is to compare this tool with others and weigh pros and cons.

The open question here is who does this effect. If the result is 1000 wrong arrests out of 10mil, that might be acceptable. But if ever one of those arrests is that 1 poor guy who get's arrested every time he steps out of his house because his face is a universal match, that's a very different result. Ditto if this leads to large numbers of incorrect arrests in certain groups (race, gender, age etc).

This is the problem with anything involving people: distributions matter a lot and distributions are hard...