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 “professional 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.

Edit

Just as a test, because I myself got curious; I had Stable Diffusion generate 20 images given the prompt

professional person dressed in business attire, smiling

20 sampling steps, using DPM++ 2M SDE Karras, and the v1-5-pruned-emaonly Stable Diffusion model.

Here’s the result

I changed the prompt to

professional person dressed in business attire, smiling, [diverse, diversity]

And here is the result

The models can generate non-white men, but it is in a way just a reflection of our society. White men are the default. Likewise if you prompt it for “loving couple” there’ll be so many images of straight couples. But don’t just take my word for it, here’s an example.