Look, I hate racism and inherent bias toward white people but this is just ignorance of the tech. Willfully or otherwise it’s still misleading clickbait. Upload a picture of an anonymous white chick and ask the same thing. It’s going go to make a similar image of another white chick. To get it to reliably recreate your facial features it needs to be trained on your face. It works for celebrities for this reason not a random “Asian MIT student” This kind of shit sets us back and makes us look reactionary.
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It’s less a reflection on the tech, and more a reflection on the culture that generated the content that trained the tech.
Wang told The Globe that she was worried about the consequences in a more serious situation, like if a company used AI to select the most "professional" candidate for the job and it picked white-looking people.
This is a real potential issue, not just “clickbait”.
If companies go pick the most professional applicant by their photo that is a reason for concern, but it has little to do with the image training data of AI.
A company using a photo to choose a candidate is really concerning regardless if they use AI to do it.
Some people (especially in business) seem to think that adding AI to a workflow will make obviously bad ideas somehow magically work. Dispelling that notion is why articles like this are important.
(Actually, I suspect they know they’re still bad ideas, but delegating the decisions to an AI lets the humans involved avoid personal blame.)
It still perfectly and visibly demonstrates the big point of criticism in AI: The tendencies the the training material inhibits.
You said yourself you hate inherent bias yet attempt to justify the result by saying if used again it’s just going to produce another white face.
that’s the problem
It’s a racial bias baked into these AIs based on their training models.
I doubt it is concious racial bias, it's most likely that the training data is made up of mostly white people and labeled poorly.
They aren't justifying anything, they literally said it was about the training data.
This is like a demonstration of lack of self awareness
Meanwhile every trained model on Civit.ai produces 12/10 Asian women...
Joking aside, what you feed the model is what you get. Model is trained. You train it on white people, it's going to create white people, you train it on big titty anime girls it's not going to produce WWII images either.
Then there's a study cited that claims Dall-e has a bias when producing images of CEO or director as cis-white males. Think of CEOs that you know. Better yet, google them. It's shit but it's the world we live in. I think the focus should be on not having so many white privileged people in the real world, not telling AI to discard the data.
They should just call AIs “confirmation bias amplifiers”.
This is not surprising if you follow the tech, but I think the signal boost from articles like this is important because there are constantly new people just learning about how AI works, and it's very very important to understand the bias embedded into them.
It's also worth actually learning how to use them, too. People expect them to be magic, it seems. They are not magic.
If you're going to try something like this, you should describe yourself as clearly as possible. Describe your eye color, hair color/length/style, age, expression, angle, and obviously race. Basically, describe any feature you want it to retain.
I have not used the specific program mentioned in the article, but the ones I have used simply do not work the way she's trying to use them. The phrase she used, "the girl from the original photo", would have no meaning in Stable Diffusion, for example (which I'd bet Playground AI is based on, though they don't specify). The img2img function makes a new image, with the original as a starting point. It does NOT analyze the content of the original or attempt to retain any features not included in the prompt. There's no connection between the prompt and the input image, so "the girl from the original photo" is garbage input. Garbage in, garbage out.
There are special-purpose programs designed for exactly the task of making photos look professional, which presumably go to the trouble to analyze the original, guess these things, and pass those through to the generator to retain the features. (I haven't tried them, personally, so perhaps I'm giving them too much credit...)
If it's stable diffusion img2img, then totally, this is a misunderstanding of how that works. It usually only looks at things like the borders or depth. The text based prompt that the user provides is otherwise everything.
That said, these kinds of AI are absolutely still biased. If you tell the AI to generate a photo of a professor, it will likely generate an old white dude 90% of the time. The models are very biased by their training data, which often reflects society's biases (though really more a subset of society that created whatever training data the model used).
Some AI actually does try to counter bias a bit by injecting details to your prompt if you don't mention them. Eg, if you just say "photo of a professor", it might randomly change your prompt to "photo of a female professor" or "photo of a black professor", which I think is a great way to tackle this bias. I'm not sure how widespread this approach is or how effective this prompt manipulation is.
Garbage in = Garbage out
ML training data sets are only as good as their data, and almost all data is inherently flawed. Biases are just more pronounced in these models because they scale the bias with the size of the model, becoming more and more noticeable.
Can we talk about how a lot of these AI-generated faces have goat pupils? That's some major bias that is often swept under the rug. An AI that thinks only goats can be professionals could cause huge disadvantages for human applicants.
These biases have always existed in the training data used for ML models (society and all that influencing the data we collect and the inherent biases that are latent within), but it’s definitely interesting that generative models now make these biases much much more visible (figuratively and literally with image models) to the lay person
Also depends on what model was used, prompt, strength of prompt etc.
No news here, just someone who doesn't know how to use AI generation.
Why is anyone surprised at this? People are using AI for things it was never designed and optimized for.
Hm. Probably trained on more white people than Asians shrug
She asked the AI to make her photo more like what society stereotypes as professional, and it made her photo more like what society stereotypes as professional.
Garbage post
Ask AI to generate an image of a basketball player and see what happens.
This isn't some OMG ThE CoMpUtER Is tHe rAcIsT... this is using historical data and using that data to alter or generation a new image. But our news media will of course try to turn it into some clickbait BS.
Racial bias propagating, click-baity article.
Did anyone bother to fact check this? I ran her exact photo and prompt through Playground AI and it pumped out a bad photo of an Indian woman. Are we supposed to play the raical bias card against Indian women now?
This entire article can be summarized as "Playground AI isn't very good, but that's boring news so let's dress it up as something else"
This is why you get a human to take professional shots for you.
Media: "I don't understand technology" even though writing about the technology multiple times.
AIs are completely based on the training data that they'll use. If they only loaded professional headshots of Asian people, a white person would turn Asian if added.
Besides which you run it multiple times, and choose the one you want, I'm sure if you did that, it'd change her eye color multiple times.
Really blame the AI, not AI in general. Or blame the media for making clickbait articles in the first place.
Honestly news stories about dumb ideas not working out don't really bother me much. Congrats, the plagiarism machine tried to make you look like you fit in to a world that, to the surprise of nobody but idealists, still has a shitload of racial preferences.
Honestly it's just not being used correctly. I actually believe this is just user error.
These AI image creators rely on the base models they were trained with and more than likely were fed wayyyyy more images of Caucasians than anyone else. You can add weights to what you would rather see in your prompts, so while I'm not experienced with the exact program she used, the basics should be the same.
You usually have 2 sections, the main prompt (positive additions) and a secondary prompt for negatives, things you don't want to see. An example prompt could be "perfect headshot for linked in using supplied image, ((Asian:1.2))" Negative: ((Caucasian)), blue eyes, blonde, bad eyes, bad face, etc....
If she didn't have a secondary prompt for negatives I could see this being a bit more difficult, but still there are way better systems to use then. If she didn't like the results from the one she used instead of jumping to "AI racism!" she could have looked up what other systems exist. Hell, with the model I use with Automatic1111 I have to put Asian in my negatives because it defaults to that often.
Edit: figures I wrote all this then scrolled down and noticed all the comments saying the same thing lol at least we're on the same page
So? There are white people in the world. Ten bucks says she tuned it to make her look white for the clicks. I've seen this in person several times at my local college. People die for attention, and shit like this is an easy-in.
Sigh...
It's not racial bias, it works from a limited dataset and what it understands a "professional headshot" even is.
Seems like some ragebait to me.
It isn't intended to be racial bias, it just happens to be racial bias.
This is just dumb rage-bait. At worst this shows a bias in training data, probably because the AI was developed in a majority white country that used images of majority white people to train it.
And likely its not even that. The AI has no concept of race, so doesnt know to make white people white and asian people asian, so would also be likely to do the reverse.
Interestingly, many stable diffusion models are trained on pictures of Asian people and thus often generate people that look more or less Asian if there's no specific input or tuning otherwise. It's all in the training data and tuning.
See for example here for reference.