There are efforts to make more accurate classifiers (2021), which seems to come down to avoiding dataset biases, but for them to work, they essentially have to

There are efforts to make more accurate classifiers (2021), which seems to come down to avoiding dataset biases, but for them to work, they essentially have to group humans into racial subsets (of which 'gorilla' and 'chimpanzee' might be a discrete entity, which is evolutionarily interesting at least, as humans/chimps/gorillas are more closely related than to each other relative any outgroup). https://www.mdpi.com/2227-7390/9/2/195 https://www.mdpi.com/2227-7390/9/2/195 > "Studies have shown that due to the distribution of ethnicity/race in training datasets, biometric algorithms suffer from “cross race effect”—their performance is better on subjects closer to the “country of origin” of the algorithm." However, these classifiers are also problematic as racial identification is a bit thorny politically. It's comparable to gender identification classifiers in the context of trans politics (as, regardless of personal identification, there are some classifier-accessible differences between the vast majority of female and male faces, although I suppose there's an androgenous middle ground). To go out on a limb a little, imagine an 'Aryan vs. Jewish' classifier - who wants to be associated with that? Lots of downsides for any commercial outfit, certainly, and few upsides.