Ok, but she asked it to make her look professional and the only thing it changed was her race. Not the background, not her clothes. Last I checked, a university sweatshirt wasn’t exactly professional wear.
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.
Sounds like they fucked up training the AI then. For a user it doesn’t matter whether the AI is designed poorly or trained poorly, it’s behaving poorly.
That’s because the denoising was set low. You can tell that it did actually modify her sweatshirt and the background. The model is just not able to turn her sweatshirt into a blazer, and keep her face relatively similar.
To do this kind of editing, you add noise to the image and get the model to remove the noise, painting in new details. To fully change clothes, you would have to add so much noise that you would lose the original image entirely and end up getting a completely different person, background, pose, everything.
We shouldn’t be surprised that race changed. The model didn’t know what race she was in the first place. It was just told to ‘change the image according to these prompts’ with about this |_| much wiggle room.
Machine learning is biased towards its training data. If the image generation algorithm (notice I’m not saying AI) is trained on photos of “” professionals " being of a certain demographic that’s what it will prefer when it’s generating an image.
So these shocking exposés should simply be this image generator was trained with biased data. But the human condition is building biases. So we’re never really going to get away from that.
Playground AI founder Suhail Doshi said that “models aren’t instructable like that” and will pick “any generic thing based on the prompt.” However, he said in another tweet that Playground AI is “quite displeased with this and hope to solve it.”
So the model wasn’t even designed to be used in the way she was trying to use it.
Half of the outrage against ai models can be attributed to the users not even understanding what they are doing. Like when people complain about ChatGPT giving wrong information, when warnings about it are written right there on the page where users are typing in their prompts.
Ok, but she asked it to make her look professional and the only thing it changed was her race. Not the background, not her clothes. Last I checked, a university sweatshirt wasn’t exactly professional wear.
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.
It’s ok to admit that you don’t understand how the models were trained and that it in no way “screwed up”.
Lol, as though that’s ever going to happen. It’s a nice thought though, I’ll join you in hoping that we’ll one day live to witness such a miracle.
Sounds like they fucked up training the AI then. For a user it doesn’t matter whether the AI is designed poorly or trained poorly, it’s behaving poorly.
Have a lovely day.
That’s because the denoising was set low. You can tell that it did actually modify her sweatshirt and the background. The model is just not able to turn her sweatshirt into a blazer, and keep her face relatively similar.
To do this kind of editing, you add noise to the image and get the model to remove the noise, painting in new details. To fully change clothes, you would have to add so much noise that you would lose the original image entirely and end up getting a completely different person, background, pose, everything.
We shouldn’t be surprised that race changed. The model didn’t know what race she was in the first place. It was just told to ‘change the image according to these prompts’ with about this |_| much wiggle room.
Machine learning is biased towards its training data. If the image generation algorithm (notice I’m not saying AI) is trained on photos of “” professionals " being of a certain demographic that’s what it will prefer when it’s generating an image.
So these shocking exposés should simply be this image generator was trained with biased data. But the human condition is building biases. So we’re never really going to get away from that.
Playground AI founder Suhail Doshi said that “models aren’t instructable like that” and will pick “any generic thing based on the prompt.” However, he said in another tweet that Playground AI is “quite displeased with this and hope to solve it.”
So the model wasn’t even designed to be used in the way she was trying to use it.
Half of the outrage against ai models can be attributed to the users not even understanding what they are doing. Like when people complain about ChatGPT giving wrong information, when warnings about it are written right there on the page where users are typing in their prompts.