An Asian MIT student asked AI to turn an image of her into a professional headshot. It made her white with lighter skin and blue eyes.::Rona Wang, a 24-year-old MIT student, was experimenting with the AI image creator Playground AI to create a professional LinkedIn photo.
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.
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.
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.
Especially ones that are still heavily in development
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.)
Businesses will continue to use bandages rather than fix their root issue. This will always be the case.
I work in factory automation and almost every camera/vision system we’ve installed has been a bandage of some sort because they think it will magically fix their production issues.
We’ve had a sales rep ask if our cameras use AI, too. 😵💫
It’s a massive issue that many people (especially in business) have this “the AI has spoken”-bias.
Similar to how they implement whatever the consultant says, no matter if it actually makes sense, they just blindly follow what the AI says .
Hiring practices are broken from its very basics. The vast majority of businesses consistently discriminate against people who deviate from the norm in presentation, even if the candidate meets the technical requirements or would otherwise be productive, which results in millions of people who are capable of contributing to society being pushed aside.
Again, that’s not really the case.
I have Asian friends that have used these tools and generated headshots that were fine. Just because this one Asian used a model that wasn’t trained for her demographic doesn’t make it a reflection of anything other than the fact that she doesn’t understand how MML models work.
The worst thing that happened when my friends used it were results with too many fingers or multiple sets of teeth 🤣
No company would use ML to classify who’s the most professional looking candidate.
- Anyone with any ML experience at all knows how ridiculous this concept is. Who’s going to go out there and create a dataset matching “proffesional looking scores” to headshots?
- The amount of bad press and ridicule this would attract isn’t worth it to any company.
Companies already use resume scanners that have been found to bias against black sounding names. They’re designed to feedback loop successful candidates, and guess what shit the ML learned real quick?
The AI might associate lighter skin with white person facial structure. That kind of correlation would need to be specifically accounted for I’d think, because even with some examples of lighter skinned Asians, the majority of photos of people with light skin will have white person facial structure.
Plus it’s becoming more and more apparent that AIs just aren’t that good at what they do in general at this point. Yes, they can produce some pretty interesting things, but they seem to be the exception rather than the norm, and in hindsight, a lot of my being impressed with results I’ve seen so far is that it’s some kind of algorithm that is producing that in the first place when the algorithm itself isn’t directly related to the output but is a few steps back from that.
I bet for the instances where it does produce good results, it’s still actually doing something simpler than what it looks like it’s doing.
It still perfectly and visibly demonstrates the big point of criticism in AI: The tendencies the the training material inhibits.
Hm. Probably trained on more white people than Asians shrug
Garbage post
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.
I’ve taken a look at the website for the one she used and it looks like a cheap crap toy. It’s free, which is the first clue that it’s not going to be great.
Not a million miles from the old “photo improvement” things that just run a bunch of simple filters and make over-processed HDR crap.
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”
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.
Yeah there are a lot of cases of claims being made of AI “bias” which is in fact just a reflection of the real world (from which it was trained). Forcing AI to fake equal representation is not fixing a damn thing in the real world.
Cool let’s just focus on skin color. If you’re white you shouldn’t be in power cause my racism is better than your racism. How about we judge people by their quality of work instead of skin color. I thought that was the whole point.
Also sure, let’s judge male white CEOs on merit. Let’s start with Elon Musk…
Also I can’t understand why there are people here assuming that the only way to “focus on having less white male CEOs” == eliminating them. This shit is done organically. Eliminating wage gap, providing equal opportunities in education etc.
I recall the being a study of the typical CEO. 6+ feet tall, white males.
But yeah, the output she was getting really depends heavily on the data that whatever model she used was trained on. For someone who is a computer science major, I’m surprised she simply cried “racial bias” rather than investigating the why, and how to get the desired results. Like cranking down the denoising strength.
To me it just seems like she tried messing around with those easy to use, baity websites without really understanding the technology.
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How did you get from what I wrote to “tearing down” anyone is a bit puzzling. It’s simply about striving to change the status quo and not the AI model representing it. I’m not advocating guillotining Bezos or Musk, hope that’s clear.
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They should just call AIs “confirmation bias amplifiers”.
Stereotype machines
Humans will identify sterotypes in AI generated materials that match the dataset.
Assume the dataset will grow and eventually mimic reality.
How will the law handle discrimination based on data supported sterotypes?
Assume the dataset will grow and eventually mimic reality.
How would that happen, exactly?
Stereotypes themselves and historical bias can bias data. And AI trained on biased data will just learn those biases.
For example, in surveys, white people and black people self-report similar levels of drug use. However, for a number of reasons, poor black drug users are caught at a much higher rate than rich white drug users. If you train a model on arrest data, it’ll learn that rich white people don’t use drugs much but poor black people do tons of drugs. But that simply isn’t true.
The datasets will get better because people have started to care.
Historically much of the data used was what was easy and cheap to acquire. Surveys of class mates. Arrest reports. Public available, government curated data.
Good data costs money and time to create.
The more people fact check, the more flaws can be found and corrected. The more attention the dataset gets the more funding is likely to come to resurvey or w/e.
It part of the peer review thing.
It’s not necessarily a matter of fact checking, but of correcting for systemic biases in the data. That’s often not the easiest thing to do. Systems run by humans often have outcomes that reflect the biases of the people involved.
The power of suggestion runs fairly deep with people. You can change a hiring manager’s opinion of a resume by only changing the name at the top of it. You can change the terms a college kid enrolled in a winemaking program uses to describe a white wine using a bit of red food coloring. Blind auditions for orchestras result in significantly more women being picked than unblinded auditions.
Correcting for biases is difficult, and it’s especially difficult on very large data sets like the ones you’d use to train chatgpt. I’m really not very hopeful that chatgpt will ever reflect only justified biases, rather than the biases of the broader culture.
That’s just stupid and shows a lack of understanding of how this all works.
AI learns what is in the data.
The AI we have isn’t “learning”. They are pre-trained.
The “pre-training” is learning, they are often then fine-tuned with additional training (that’s the training that isn’t the ‘pre-training’), i.e. more learning, to achieve specific results.
User error, be more specific
Yeah they forgot to say “don’t change my ethnicity” to the prompt. Normal shit, right?
“Don’t change my ethnicity” would do nothing, as these programs can not get descriptions from images, only create images from descriptions. It has no idea that the image contains a woman, never mind an Asian woman. All it does is use the image as a starting point to create a “professional photo”. There absolutely is training bias and the fact that everyone defaults to pretty white people in their 20-30s is a problem. But this is also using the tool badly and getting a bad result.
It would be the same if the user wanted to preserve or highlight any other feature, simply specify what the output needs to look like. Ask for nothing but linkedin professional and you get the average linkedin professional.
It’s like being surprised the output looks asian when asking to look like a wechat user
AI turned her into a White Walker
I read this as a “White Wanker”
I read Walter White, i’m going to sleep now.
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.
Disappointing but not surprising. The world is full of racial bias, and people don’t do a good job at all addressing this in their training data. If bias is what you’re showing the model, that’s exactly what it’ll learn, too.
Why is anyone surprised at this? People are using AI for things it was never designed and optimized for.
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.
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.
Just for fun, I did.
Prompt: basketball player, gym, standing in gym, basketball hoop, sports uniform, facing camera, smiling
Model: base stable diffusion 1.5
That image highlights an important point, these AI produce an infinite number of images for any given prompt. It’s easy to pick one and make conclusions based on just one, like this this article did, but you’re literally ignoring infinity other images produced for the same prompt.
This was the first one I generated. I could keep generating and get different results.
I could add a LoRA and get another infinity worth of different results.
Actually though… is it infinity? Or just a very high number based on the seed?
My intuition says that it’s just a very high number, because there’s a finite variation of initial noise and a finite seed number.
Finite, but big enough that you won’t get exact repeats any time soon. For a 32-byte seed and a reasonably uniform PRNG, any time before the Sun enters its red giant phase and absorbs the Earth.
