It’s not, the underlying data is still just as biased. Taking a bunch of white people and saying they are “ethnically ambiguous” is just statistical blackface.
I agree with you, but there is a lot of gray area. What about Spider-man? 95% of the pictures it ingests are probably Peter Parker so it would have a strong bias towards making him white when there are several ethnicities that might apply. What about Katniss Everdeen? Is she explicitly white in the book or is she just white because she’s played by a white actress? I truly don’t know so maybe that is a bad example. What about Santa? What about Jesus? Of all characters, Jesus absolutely shouldn’t be white but I’ll bet the vast majority of AI depicts him that way.
I’m not disagreeing with you so much as I’m pointing out the line isn’t really all that clear. I don’t like this ham-handed way of going about it, but I agree with and support the goal of making sure the output isn’t white biased just because preserved history tends to be.
It’s tricky because the data itself is going to be biased here. Think about it - even the video game is specifically called “Spider-Man Miles Morales” while the one with Peter Parker is just called “Spider-Man.”
Katniss is actually a good example. I was not aware of the details, but the books apparently describe her as having “olive skin”. The problem though is that if you image search her all you get is Jennifer Lawrence.
If you were really curious about the answer, you practically gave yourself the right search term there: “racial bias in general purpose LLM” and you’ll find answers.
However, like your question is phrased, you just seem to be trolling (= secretly disagreeing and pretending to wanting to know, just to then object).
That’s actually a pretty smart way to combat racial bias.
Except when it does this
No, it’s an incredibly dumb way because fucking with people’s prompts will make the tech unreliable
Man, do I have some bad news for you
Lol fair enough. I guess I could say “make the tech even less reliable”
The smarter way would be using balanced training data.
You can’t balance every single aspect of the training data. You will always run into some searches favoring one race over another.
It’s not, the underlying data is still just as biased. Taking a bunch of white people and saying they are “ethnically ambiguous” is just statistical blackface.
If a request is for a generic person, sure. But when the request is for a specific character, not really.
Like make one of the undefined arms black.
I agree with you, but there is a lot of gray area. What about Spider-man? 95% of the pictures it ingests are probably Peter Parker so it would have a strong bias towards making him white when there are several ethnicities that might apply. What about Katniss Everdeen? Is she explicitly white in the book or is she just white because she’s played by a white actress? I truly don’t know so maybe that is a bad example. What about Santa? What about Jesus? Of all characters, Jesus absolutely shouldn’t be white but I’ll bet the vast majority of AI depicts him that way.
I’m not disagreeing with you so much as I’m pointing out the line isn’t really all that clear. I don’t like this ham-handed way of going about it, but I agree with and support the goal of making sure the output isn’t white biased just because preserved history tends to be.
It’s tricky because the data itself is going to be biased here. Think about it - even the video game is specifically called “Spider-Man Miles Morales” while the one with Peter Parker is just called “Spider-Man.”
Katniss is actually a good example. I was not aware of the details, but the books apparently describe her as having “olive skin”. The problem though is that if you image search her all you get is Jennifer Lawrence.
That said, Homer is yellow.
Not when the prompt includes a named character.
Can you explain to me how racial bias in general-purpose LLM is a problem to begin with?
If you were really curious about the answer, you practically gave yourself the right search term there: “racial bias in general purpose LLM” and you’ll find answers.
However, like your question is phrased, you just seem to be trolling (= secretly disagreeing and pretending to wanting to know, just to then object).