I just tried it out and it looks like DALL-E isn't as inept as you imagined. Exact query used was 'A profile photo of a male south korean CEO', and it spat out 4 very believable korean business dudes.
Supplying the race and sex information seems to prevent new keywords from being injected. I see no problem with the system generating female CEOs when the gender information is omitted, unless you think there are?
I don't think they "randomly insert keywords" like people are claiming, I think they probably run it through a GPT3 prompt and ask it to rewrite the prompt if it's too vague.
I set up a similar GPT prompt with a lot more power ("rewrite this vague input into a precise image description") and I find it much more creative and useful than DALLE2 is.
> If you want an image of a <race> <gender> person included, you can just specify it yourself.
I agree wholeheartedly. So what are we arguing about?
What we're seeing is that DALL-E has its own bias-balancing technique it uses to nullify the imbalances it knows exists in its training data. When you specify ambiguous queries it kicks into action, but if you wanted male white CEOs the system is happy to give it to you. I'm not sure where the problem is.
Supplying the race and sex information seems to prevent new keywords from being injected. I see no problem with the system generating female CEOs when the gender information is omitted, unless you think there are?