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.
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.
I posted my sides across many comments. But the same argument applies to everyone saying the opposite.
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