this post was submitted on 24 Feb 2024
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The students should get together and jack the machine away into their hacking club and do some reverse engineering, so that we get more information on how the data collection worked as opposed to just trusting the company's statements. If a hacking group like the German Chaos Computer Club got behind this, they could release their findings while keeping the perpetrators anonymous. However, I’m pretty sure the machine is just a frontend to a server, which got shut down as soon as the students complained, with no GDPR-like checkout being available in the jurisdiction.
When you start tinkering with a machine learning model of any kind, you’re probably going to find some interesting edge cases the model can’t handle correctly. Maybe there’s a specific face that has an unexpected effect on the device. What if you could find a way to cheese a discount out of it or something?
Imagine a racist vending machine. The face recognition system think this customer is black with 81% confidence. Let's increase the price of grape soda! Oh look, a 32 year old white woman (79% confidence). Better raise the price of diet coke!
In Japan they had some kind of facial recognition on vending machines selling cigarettes that would determine the age of the person in attempt to prevent kids from buying cigarettes. But it only worked for Japanese people.
Stupid racist vending machine wouldn't sell me smokes!
shame. id like to send you a carton.
It's cool, I quit years ago.
Also I was in a diverse group of people and we were able to do some science. Fortunately we had a Japanese person in the group which allowed me to purchase the smokes. But yeah, it failed on everyone that wasn't Japanese.
When you use a generated face with a mixture of white and black features, that’s when it gets interesting. Maybe you can even cause an integer overflow.