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.
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.
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.)
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.
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.
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.
I also wouldn’t say it was conscious bias either. I don’t think it’s intentionally developed in that way.
The fact still remains though whether conscious or unconscious, it’s potentially harmful to people of other races. Sure it’s an issue with just graphic generation now. What about when it’s used to identify criminals? When it’s used to filter between potential job candidates?
The possibilities are virtually endless, but if we don’t start pointing out and addressing any type of bias, it’s only going to get worse.
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.
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.
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.
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.
They should just call AIs “confirmation bias amplifiers”.
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.
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.
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.
