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this post was submitted on 28 Jul 2023
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14 out of 15 requests were of black people. Facial recognition is notoriously bad with darker skin tones.
Racial Discrimination in Face Recognition Technology https://sitn.hms.harvard.edu/flash/2020/racial-discrimination-in-face-recognition-technology/
Yeah, but statistics is a b*tch.
We had a similar technology for a test run some years ago at a train station in Berlin, capital of Germany and largest city in the EU with 3.8M.
The results the government happily touted as a success were devastating. They had a true positive rate of 80% (and this was already cooked since they tested several systems at several locations but only reported the best results), which is really not that good to start with.
But they were also extremely proud of the false ~~negative~~ positive rate, which was below 0.1%. That doesn’t sound too bad, does it?
Well, let’s see…
True positive means you actually identified the people you were looking for. Now, I don’t know the number of people Berlin’s police is actively looking for, but it’s not that much. And the chances of one of them actually passing that very station are even worse. And out of that, you have 20% undetected. That’s one out of five. Great. If I were a terrorist, I would happily take that chance.
So now let’s have a look at the false ~~negative~~ positive rate, which means you incorrectly identified a totally harmless person as a terrorist/infected/whatever. The population for that condition is: everyone passing through that station.
Let’s assume there’s a 100k people on any given day (which IIRC is roughly half of what that station in Berlin actually has). 0.1% of 100k is 100 people, every day, who are mistakenly reported as „terrorists“. Yay.
I think you’ve gotten false negative wrong here: False negatives are terrorists who were not identified as such.
D’oh! 🤦♀️ Of course, thanks for correcting this.