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If I was using this in South Korea, how is showing all white people any better than showing whites, blacks, latinos and asians?
by educaysean 4y ago
If I was using this in South Korea, how is showing all white people any better than showing whites, blacks, latinos and asians?
- bequanna 4y agoYou would presumably input “South Korean CEO”. DALL-E would then unhelpfully add “black” “female” without your knowledge.
- educaysean 4y agoI 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?
- bequanna 4y agoIsn’t the diversity keyword injection random? My point is that it is pointless. If you want an image of a <race> <gender> person included, you can just specify it yourself.
- educaysean 4y ago> 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.
- astrange 4y agoI 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.