> "They never even asked him any questions before arresting him. They never asked him if he had an alibi. They never asked if he had a red Cardinals hat. They never asked him where he was that day," said lawyer Phil Mayor with the ACLU of Michigan.
When I was fired by an automated system, no one asked if I had done something wrong. They asked me to leave. If they had just checked his alibi, he would have been cleared. But the machine said it was him, so case closed.
Not too long ago, I wrote a comment here about this [1]:
> The trouble is not that the AI can be wrong, it's that we will rely on its answers to make decisions.
> When the facial recognition software combines your facial expression and your name, while you are walking under the bridge late at night, in an unfamiliar neighborhood, and you are black; your terrorist score is at 52%. A police car is dispatched.
Most of us here can be excited about Facial Recognition technology but still know that it's not something to be deployed in the field. It's by no means ready. We might even consider the moral ethics before building it as a toy.
But that's not how it is being sold to law enforcement or other entities. It's _Reduce crime in your cities. Catch criminals in ways never thought possible. Catch terrorists before they blow up anything._ It is sold as an ultimate decision maker.
Software can kill. This software can kill 50% of black people.
Even if it was correct 99% of the time, we need to recognize that software can make mistakes. It is a tool, and people need to be responsible enough to use it correctly. I think I agree with your general idea here, but to put all of the blame on software strikes me as an incomplete assessment. Technically the software isn't killing anyone, irresponsible users of it are.
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.