If the word "Zulu" appears in a label, it will be a non-White person 100% of the time.
If the word "English" appears in a label, it will be a non-White person 10%+ of the time. Only 75% of modern England is White and most images in the training data were taken in modern times.
Image models do not have deep semantic understanding yet. It is an LLM calling an Image model API. So "English" + "Kings" are treated as separate conceptual things, then you get 5-10% of the results as non-White people as per its training data.
If the word "Zulu" appears in a label, it will be a non-White person 100% of the time.
If the word "English" appears in a label, it will be a non-White person 10%+ of the time. Only 75% of modern England is White and most images in the training data were taken in modern times.
Image models do not have deep semantic understanding yet. It is an LLM calling an Image model API. So "English" + "Kings" are treated as separate conceptual things, then you get 5-10% of the results as non-White people as per its training data.
https://postimg.cc/0zR35sC1
Add to this massive amounts of cherry picking on "X", and you get this kind of bullshit culture war outrage.
I really would have expected technical people to be better than this.