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…

Surely that depends on where in Asia you’re looking at as well? On average, the number of languages people speak is quite different between, say, India and Japan. Or Switzerland vs Romania in Europe.

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