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.

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.

1. 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?
2. The amount of bad press and ridicule this would attract isn’t worth it to any company.

Didn’t miss the point. It was trained on images of people. The majority of images it had access to were white faces because that’s what was available to scrape. Too many white people are represented in media. Isn’t that the underlying point? AI is merely reflecting that, as it was designed to do. That reflection is embarrassing. Like a toddler with a potty mouth. Not the kids fault.

They aren’t justifying anything, they literally said it was about the training data.

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.

The AI we have isn’t “learning”. They are pre-trained.

That’s just stupid and shows a lack of understanding of how this all works.

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?

You don’t really program them, they learn from the data provided. If say you want a model that generates faces, and you provide it with say, 500 faces, 470 of which are of black women, when you ask it to generate a face, it’ll most likely generate a face of a black woman.

The models are essentially maps of probability, you give it a prompt, and ask it what the most likely output is given said prompt.

If she had used a model trained to generate pornography, it would’ve likely given her something more pornographic, if not outright explicit.

You’ve also kind of touched on a point of problem with large language models; they’re not programmed, but rather prompted.

When it comes to Bing Chat, Chat GPT and others, they have additional AI agents sitting alongside them to help filter/mark out problematic content both provided by the user, as well as possible problematic content the LLM itself generates. Like this prompt, the model marked my content as problematic and the bot gives me a canned response, “Hi, I’m bing. Sorry, can’t help you with this. Have a nice day. :)”

These filters are very crude, but are necessary because of problems inherent in the source data the model was trained on. See, if you crawl the internet for data to train it on, you’re bound to bump into all sorts of good information; Wikipedia articles, Q&A forums, recipe blogs, personal blogs, fanfiction sites, etc. Enough of this data will give you a well rounded model capable of generating believable content across a wide range of topics. However, you can’t feasibly filter the entire internet, among all of this you’ll find hate speech, you’ll find blogs run by neo nazis and conspiracy theorists, you’ll find blogs where people talk about their depression, suicide notes, misogyny, racism, and all sorts of depressing, disgusting, evil, and dark aspects of humanity.

Thus there’s no code you can change to fix racism.

if (bot.response == racist) 
{
    dont();
}

But rather simple measures that read the user/agent interaction, filtering it for possible bad words, or likely using another AI model to gauge the probability of an interaction being negative,

if (interaction.weightedResult < negative)
{
    return "I'm sorry, but I can't help you with this at the moment. I'm still learning though. Try asking me something else instead! 😊";
}

As an aside, if she’d prompted “proffesional Asian woman” it likely would’ve done a better job. Depending on how much “creative license” she gives the model though, it still won’t give her her own face back. I get the idea of what she’s trying to do, and there’s certainly ways of acheiving it, but she likely wasn’t using a product/model weighted to do specifically the thing she was asking to do.

That’s not how it works. You don’t just “program out the biases” you have to retain the model with more inclusive training data.

If you can code it, it isn’t really AI.

AI is able to make the connections when given the data by itself. The problem is that the data required is usually enormous, so the quantity of data is more valued than the quality.

Shouldn’t they be able to code them out?

You can’t “code them out” because AI isn’t using a simple script like traditional software. They are giant nested statistical models that learn from data. It learns to read the data it was trained on. It learns to understand images that it was trained on, and how they relate to text. You can’t tell it “in this situation, don’t consider race” because the situation itself is not coded anywhere. It’s just learned behaviors from the training data.

Shouldn’t they be able to lessen them?

For this one the answer is YES. And they DO lessen them as much as they can. But they’re training on data scraped from many sources. You can try to curate the data to remove racism/sexism, but there’s no easy way to remove bias from data that is so open ended. There is no way to do this in an automated way besides using an AI model, and for that, you need to already have a model that understands race/gender/etc bias, which doesn’t really exist. You can have humans go through the data to try to remove bias, but that introduces a ton of problems as well. Many humans would disagree on what is biased. And human labelers also have a shockingly high error rate. People are flat out bad at repetitive tasks.

And even that only covers data that actively contains bigotry. In most of these generative AI cases, the real issue is just a lack of data or imbalanced data from the internet. For this specific article, the user asked to make a photo look professional. Training data where photos were clearly a professional setting probably came from sites like LinkedIn, which had a disproportionate number of white users. These models also have a better understanding of English than other languages because there is so much more training data available in English. So asian professional sites may exist in the training data, but the model didn’t understand the language as well, so it’s not as confident about professional images of Asians.

So you can address this by curating the training data. But this is just ONE of THOUSANDS and THOUSANDS of biases, and it’s not possible to control all of them in the data. Often if you try to correct one bias, it accidentally causes the model to perform even worse on other biases.

They do their best. But ultimately these are statistical models that reflect the existing data on the internet. As long as the internet contains bias, so will AI

It’s possible sure. In order to train these image AIs you essentially feed them a massive amount of pictures as “training data.” These biases happen because more often than not the training data used is mostly pictures of white people. This might be due to racial bias of the creators, or a more CRT explanation where they only had the rights to pictures of mostly white people. Either way, the fix is to train the AI on more diverse faces.

“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 while people in their 20-30s is a problem. But this is also using the tool badly and getting a bad result.

Yes. Or even better, just add “asian” to the prompt. It’s just a tool and tools are flawed.

Nope… because I just tried it as a white male and got back a pure Asian man using the same prompts… and I’ll be damned if I’m not jealous/sad because the man it spit back out was way better looking than me…

Which model did they use, and what was it specialized for? Was professional headshots what it was designed for? Those are the issues to be mad at. Not something as ambiguous as “AI”.