The gender bias exists due to the assumptions it makes on the sample data. I'm sure if you searched "<gender> <profession>" it would get you what you want.
If 95% of publicly available images of a profession are of one gender, should the tool be deliberately modified so that it's X% instead?
I totally agree that models that purport to represent the world can be hugely biased and reconfirming of negative biases, but how would avoiding representing those biases be achieved? Strict gender ratios in training data?
It’s more complicated this specific tool itself IMHO.
Stock photos have the same problem, where asking for a profession or a category will give mostly one kind of representation, and it’s up to the user to go dig further to find different representations.
DALL-E being on par with stock photos biases could seem benign, but it also means these issues get propagated further more down the line, and the more AI generated images get popular, the worse it gets cemented (“it has always been that way”) and could actually displace niches where better images were being used until then.
Here's one way... Given a text description, sample from the space of /more restrictive/ text descriptions (eg, add adjectives) and then draw pictures for those. Then modify the sampling space over more descriptive sentences to equalize on gender or other axes.
If 95% of publicly available images of a profession are of one gender, should the tool be deliberately modified so that it's X% instead?
I totally agree that models that purport to represent the world can be hugely biased and reconfirming of negative biases, but how would avoiding representing those biases be achieved? Strict gender ratios in training data?