Technology

More to the point, there are so many parameters that can be tweaked, to throw your image into a “generator” without knowing what controls you have, what the prompt is doing, what model it’s using etc is like saying “the internet” is toxic because you saw a webpage that had a bad word on it somewhere.

I put her actual photo into SD 1.5 (the same model you used) with 30 step Euler, 5.6 cfg and 0.7 noise and got these back. I’d say 3/4 of them are Asian (and the model had a 70% chance to influence that away if it were truly “biased” in the way the article implies), obviously none of them look like the original lady because that’s not how it works. You could generate a literally infinite number of similar-looking women who won’t look like this lady with this technique.

The issue isn’t so much that the models are biased — that is both tautologically obvious and as mentioned previously, probably preferred (rather than just randomly choosing anything not specified at all times — for instance, your monkey prompt didn’t ask for forest, so should it have generated a pool hall or plateau just to fill something in? The amount of specificity anyone would need would be way too high; people might be generated without two eyes because it wasn’t asked for, for instance); it is that the models don’t reflect the biases of all users. It’s not so much that it made bad choices but that it made choices that the user wouldn’t have made. When the user thinks “person”, she thinks “Asian person” because this user lives in Asia and that’s what her biases toward “person” start with, so seeing a model biased toward people from the Indian subcontinent doesn’t meet her biases. On top of that, there’s a general potential impossibility of having some sort of generic “all people” model given that all people are likely to interpret its biases differently.