| > What these systems describe is reality. Not quite. The "reality" (population) that an AI model represents is more like "images of the internet" rather than "population of X country" or "population of the world". (I am comparing a set of images to a set of people on purpose.) Here's a quote from an article about Stable Diffusion [1]: > For example, the model generated images of people with darker skin tones 70% of the time for the keyword “fast-food worker,” even though 70% of fast-food workers in the US are White. Similarly, 68% of the images generated of social workers had darker skin tones, while 65% of US social workers are White. > We should aim to change the world, not the resulting -- faithful -- image of that world in AI. Cure the disease, not the symptoms. Additionally, AI model companies should warn model users that images of "<member of X group>" such as "a Mexican person" are not representative of X group. Nonetheless, I would appreciate an AI model which does something along the lines of crystaln's suggestion [2]: > [If prompted for "a Nigerian person"] The algorithm could return a random Nigerian ethnic group proportional to their actual population. (I'm presuming that "a random Nigerian ethnic group" refers to "a member of a random Nigerian ethnic group".) [1] https://www.bloomberg.com/graphics/2023-generative-ai-bias/ [2] https://news.ycombinator.com/item?id=37964732 |