I’d assume that’s either due to bias in the training set, or poor design choices. The former is already a big problem in facial recognition, and can’t really be fixed unless we update datasets. With the latter, this could be using things like visible light for classification, where the contrast between target and background won’t necessarily be the same for all skin tones and times os day. Cars aren’t limited by DNA to only grow a specific type of eye, and you can still create training data from things like infrared or LIDAR. In either case though, it goes to show how important it is to test for bias in datasets and deal with it before actually deploying anything…

I do understand the curiosity though, just seeing what malware is trying to do can be quite interesting. Maybe someone should tell that person about VMs though lol

I love tea

Disappointing but not surprising. The world is full of racial bias, and people don’t do a good job at all addressing this in their training data. If bias is what you’re showing the model, that’s exactly what it’ll learn, too.