I thought about that while writing my comment, but while hair color correlates pretty well with ethnicity, the level of granularity given to Asians still seems somewhat unique. There are, for instance, no distinction between French and Italian, nor British and Scottish categories. Blonde and ginger do have fairly ethnic implications, but not many people would answer with their hair color if asked where their family originates. That, and the fact that these hair colors are not restricted to white people, due to multi-ethnicity being a thing, as well as commercial hair dye, made me drop them in my "half the ethnic labels" comment (though I will concede that the image in most people's heads when hearing any of those terms would be a white person).

This is also coming from a perspective where I'm assuming that the audience for this algorithm (outside of academic consideration) would be westerners where white people tend to be the majority. In that case fetishization generally would only apply to racial minorities, due to the fact that the term seems to imply a sexually gratifying quality to the "uncommon". I add that, speaking as an Asian American, that it's not necessarily all that easy for most people to tell Chinese from Japanese from Koreans, whereas it's basically tautologically given that a blonde can be differentiated from a brunette since the terms are more strictly aesthetic rather than geographic.

On your point about Africans, I would extend it to both Latina and the fact that "Indian" seems to be a catch-all for any South Asians. Middle Easterners are also not represented (though this doesn't strike me as a thing where racial equality is necessarily desirable). However, this is where I think the data set is mostly responsible.

I think they took the tags from some uploader website. CJK ethnicities can be inherently identified by types of sources(smuggled amateur films, commercially made, etc) as well as by styles, when appearing in those sites.

That's my guess too, but my argument is less that this arises from a specific place of malice or intention, and rather this phenomena comes from an uncritical approach to handling the dataset. There is, for instance, no reason not to group all the CJK data as "Asian" for the sake of similarity with the rest of the ethnicities. But in uncritically reflecting the dataset, it introduces downstream effects that could potentially be amplified in later work.