On surveillance through face recognition

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2021-06-13 15:30:11

A year ago, a Black man was arrested for theft. The evidence was crummy face-recognition coupled with a security guard's ID who had not seen the person, only a security tape.

A few months ago, some civil rights organizations filed a lawsuit against the Detroit Police Department for the wrongful arrest.

Let's also say it has 100% TPR (true positive rate, or "100% recall" - meaning if the guilty person is in the dataset, they will be matched also).

If you search for matches through the 1M faces, and the suspect is among those records, you get on average 1 false positive match, and 1 true positive.

So the people you are arresting only have a 1 in 2 chance of being guilty (though to be completely fair, the police would know something's up if it gets two matches, but here it was apparently just one).

If you scale this up ("50 million driver’s license photographs and mug shots contained in a Michigan police database"), you get roughly 50 false positives, and maybe one true positive, if it's in the data. But if the guilty person is not in the dataset, the police only get the false positives.

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