> And then they move on to the next topic, without making any argument why the thing they just discussed is important, or what implications it might actually have.

There are so many articles on the Internet that dive into the implications of datasets that amplify certain voices over others. I personally liked this analysis because it actually shows you what such a lopsided database looks like on the inside. And it looks like this breakdown might actually help the creators of LAION-5B fix some of the issues. I saw this as a pretty constructive and useful article.

But to your point about the implications of datasets like this one, allow me to share my personal experiences with generative AI.

(I want to make it clear that I'm not personally offended by any of the things I'm going to say below in this comment. I see all of the following as well-understood issues that can be solved if researchers/AI vendors have the will.)

I speak three languages, but I can only reliably interact with generative AI models using one of them. I'm forced to effectively erase two thirds of my culture and upbringing when I talk to ChatGPT. That severely limits what I can do with LLMs and leaves a bad taste in my mouth.

Generative AI is marketed as this giant forward leap for humanity, on par with smartphones and the Word Wide Web. But that marketing rings hollow when most people in my country can't use these tools merely because they don't speak English.

Unlike smartphones and the Web, getting the models to speak non-English languages is not such an easy task. Want to translate every single app on the iPhone into Hindi? Just hire an experienced team of translators (sidenote: the Hindi localization on iOS is excellent). But how do you create the terabytes of Hindi language content required to make ChatGPT understand Hindi as well as it understands English? I don't know what the solution is, but it's definitely a problem that needs to be solved if we want AI to be useful to as many people as possible.

Beyond LLMs, other generative models also reflect their training data in a way that favors certain cultures over others. E.g when I ask ChatGPT to "generate an image of a girl sitting on a bench, eating an apple", it generates an image of a person who is clearly white, dressed in blue jeans and sneakers. Why does it make that assumption when, in 2024, there are more brown people on the planet than white people?

If I refine that prompt to "generate an image of an indian girl sitting on a bench, eating an apple", it makes the person look more Indian but keeps the jeans and sneakers. Once again, why does the model assume that all humans wear jeans and sneakers by default? Why do I have to prompt it to generate other kinds of clothing?

AI is unlike any other technology we've built so far. We talk to it as if we're addressing a human being, and it talks back to us. This makes it difficult to treat it as merely a tool, even though all of us on this website know that's what it is. And when this anthropomorphized genie in a box refuses to speak my language or treats my culture as a second-class citizen, it stings. It's this sort of stuff that can make people feel like there's something wrong with their culture, their ethnicity, their language.

I'm sure we'll iron out all these issues eventually. But we have to keep making noise and keep demanding better from AI researchers and companies. And articles like this one really contribute a lot towards that goal.