And overall in I’d say… 7 out of 10 images this is a white woman in a Google search. So the probability is high that the training data also has a bias towards that.
Someone in the original lemmy.nz post said they did the exact same thing, same image, same prompt, and it turned her Indian. So if you have very wide training data the result would be rather “random”. Or you have very narrow training data and the result will always be looking similar.
Grab an app focused on an Asian audience with beauty filters for example and it will turn a white person into an Asian one. But no one complains there that the app is racist.
It doesn’t really matter that it was her in this image. When you put “professional” into it then you can expect something along these results:
https://www.google.com/search?q=professional+woman
And overall in I’d say… 7 out of 10 images this is a white woman in a Google search. So the probability is high that the training data also has a bias towards that.
Someone in the original lemmy.nz post said they did the exact same thing, same image, same prompt, and it turned her Indian. So if you have very wide training data the result would be rather “random”. Or you have very narrow training data and the result will always be looking similar.
Grab an app focused on an Asian audience with beauty filters for example and it will turn a white person into an Asian one. But no one complains there that the app is racist.
Notice how not a single woman there is wearing a university sweatshirt.
My point still stands. It didn’t touch her clothing to make it more “professional.” Just her race. It screwed up on multiple levels here.