A big biometric security company in the UK, Facewatch, is in hot water after their facial recognition system caused a major snafu - the system wrongly identified a 19-year-old girl as a shoplifter.
A big biometric security company in the UK, Facewatch, is in hot water after their facial recognition system caused a major snafu - the system wrongly identified a 19-year-old girl as a shoplifter.
And a lot of these face recognition systems are notoriously bad with dark skin tones.
Shit even the motion sensors on the automated sinks have trouble recognizing dark skinned people! You have to show your palm to turn the water on most times!
Technically, there’s a tendency for them to be trained on datasets that don’t include nearly enough dark-skinned people. As a result, they don’t learn to make the necessary distinctions. I’d like to think that the selection of datasets for training facial recognition AI has improved since the most egregious cases of that. I’m not willing to bet on it, though.
No they aren’t. This is the narrative that keeps getting repeated over and over. And the citation for it is usually the ACLU’s test on Amazon’s Rekognition system, which was deliberately flawed to produce this exact outcome (people years later still saying the same thing).
The top FR systems have no issues with any skin tones or connections.
There are like a thousand independent studies on this, not just one
I promise I’m more aware of all the studies, technologies, and companies involved. I worked in the industry for many years.
The technical studies you’re referring to show that the difference between a white man and a black woman (usually polar opposite in terms of results) is around 0.000001% error rate. But this usually gets blown out of proportion by media outlets.
If you have white men at 0.000001% error rate and black women at 0.000002% error rate, then what gets reported is “facial recognition for black women is 2 times worse than for white men”.
It’s technically true, but in practice it’s a misleading and disingenuous statement.
Edit: here’s the actual technical report if anyone is interested
https://pages.nist.gov/frvt/reports/1N/frvt_1N_report.pdf
Would you kindly link some studies backing up your claims, then? Because nothing I’ve seen online has similar numbers to what you’re claiming
https://pages.nist.gov/frvt/reports/1N/frvt_1N_report.pdf
It’s a
481443 page report directly from the body that does the testing.Edit: mistyped the number of pages
Edit 2: as I mentioned in another comment. I’ve read through this document many times. We even paid a 3rd party to verify our interpretations.
It saddens me that you are being downvoted for providing a detailed factual report from an authoritative source. I apologise in the name of all Lemmy for these ignorant people
Ya, most upvotes and downvotes are entirely emotionally driven. I knew I would get downvoted for posting all this. It happens on every forum, Reddit post, and Lemmy post. But downvotes don’t make the info I share wrong.
Just post the sources first, arguing emotionally with ‘trust me bro’ should get the exact response it’s gotten.
Thanks! Appreciate it, will take a look when I have time
Np.
As someone else pointed out in another comment. I’ve been saying the x% accuracy number incorrectly. It’s just a colloquial way of conveying the accuracy. The truth is that no one in the industry uses “percent accuracy” and instead use FMR (false match rate) and FNMR (false non-match rate) as well as some other metrics.
Fair. But you are asking us to trust your word when you could provide us with some links.
https://pages.nist.gov/frvt/reports/1N/frvt_1N_report.pdf
Yep, classic fallacy (? Bias?) of consider relative scales/change over absolute.
Here are some sources that speak about the difference between the two, and how different interpreters of data can use either or to further an argument:
https://dataschool.com/misrepresenting-data/relative-vs-absolute-change/
https://stats.mom.gov.sg/SL/Pages/Absolute-vs-Relative-Change-Pitfalls.aspx
https://www.designreview.byu.edu/collections/design-in-data-figures-absolute-versus-relative-scales