Some image generators produce more problematic stereotypes than others, but all fail at diversity
algorithmwatch.org
Automated image generators are often accused of spreading harmful stereotypes, but studies usually only look at MidJourney. Other tools make serious efforts to increase diversity in their output, but effective remedies remain elusive.
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Actually it is trained on data 'the trainers have'. This is different from 'trained on data that "is"' or any other idealized view of data.
Data that 'the trainers have' is always an incomplete view of anything, and adding meaningfully to datasets is always very difficult.
I may have oversimplified my statement. Of course an objective description of reality is impossible. A curse on all social sciences and statistics.
My post was more a showerthought...even if the data is incomplete, whatever THAT data implies will also be the stereotype the AI will learn. Misrepresentation of minorities in sample data is absolutely nothing new. But even if the data WAS complete, it would probably still be very biased. I think we often don't notice structural discrimination and AI would simply reproduce those and confront us with it. In that sense I think it is a very interesting way to get a sort of 'outside look' at our own society and that is something that's very useful.