Comment by itsoktocry
1 year ago
>There is an _actual problem_ that needs to be solved. If you ask generative AI for a picture of a "nurse", it will produce a picture of a white woman 100% of the time
Why is this a "problem"? If you want an image of a nurse of a different ethnicity, ask for it.
The problem is that it can reinforce harmful stereotypes.
If I ask an image of a great scientist, it will probably show a white man based on past data and not current potential.
If I ask for a criminal, or a bad driver, it might take a hint in statistical data and reinforce a stereotype in a place where reinforcing it could do more harm than good (like a children book).
Like the person you're replying to, it's not an easy problem, even if in this case Google's attempt is plain absurd. Nothing tells us that a statistical average in the training data is the best representation of a concept
If I ask for a picture of a thug, i would not be surprised if the result is statistically accurate, and thus I don’t see a 90-year-old white-haired grandma. If I ask for a picture of an NFL player, I would not object to all results being bulky men. If most nurses are women, I have no objection to a prompt for “nurse” showing a woman. That is a fact, and no amount of your righteousness will change it.
It seems that your objection is to using existing accurate factual and historical data to represent reality? That really is more of a personal problem, and probably should not be projected onto others?
You conveniently use mild examples when I'm talking about harmful stereotypes. Reinforcing bulky NFL players won't lead to much, reinforcing minorities stereotypes can lead to lynchings or ethnic cleansing in some part of the world.
I don't object to anything, and definitely don't side with Google on this solution. I just agree with the parent comment saying it's a subtle problem.
By the way, the data fed to AIs is neither accurate nor factual. Its bias has been proven again and again. Even if we're talking about data from studies (like the example I gave), its context is always important. Which AIs don't give or even understand.
And again, there is the open question of : do we want to use the average representation every time? If I'm teaching to my kid that stealing is bad, should the output be from a specific race because a 2014 study showed they were more prone to stealing in a specific American state? Does it matter in the lesson I'm giving?
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> If most nurses are women, I have no objection to a prompt for “nurse” showing a woman.
But if you're generating 4 images it would be good to have 3 women instead of four, just for the sake of variety. More varied results can be better, as long as they're not incorrect and as long as you don't get lectured if you ask for something specific.
From what I understand, if you train a model with 90% female nurses or white software engineers, it's likely that it will spit out 99% or more female nurses or white software engineers. So there is an actual need for an unbiasing process, it's just that it was doing a really bad job in terms of accuracy and obedience to the requests.
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Because then it refuses to comply?
right? UX problem masqueraded as something else
always funniest when software professionals fall for that
I think google’s model is funny, and over compensating, but the generic prompts are lazy
One of the complaints about this specific model is that it tends to reject your request if you ask for white skin color, but not if you request e.g. asians.
In general I agree the user should be expected to specify it.
How to tell someone is white and most likely lives in the US.