The model learning bias isn't the issue. You could ask me what I think the racist members of my family might write about a given image. I'd then be able to emulate them inside my head and accurately predict their responses. We all do that. It's how we have moments like "I knew you were going to say that."; "That's typical of you to say."; "Why am I not surprised?"; etc. The fact that I, and everyone else, can do that does not imply that we are biased. It's how we behave that determines if we are biased.
We want our AIs to do the same.
The real ethics question here is not how to we prevent AIs from learning bias. It's how do we get AIs to not _express_ those biases. We need a way to put them into "impartial" mode, much like we take biased and fallible humans and make them judges in courtrooms.
Personally I don't think that's going to be as hard as some imagine. Again, remember that these AIs are learning to emulate humans, _including_ judges. Give GPT-* a bunch of court documents and transcripts and it will learn the capacity to emulate a judge. Then you just need to carefully craft its prompt text so that for any given query, you can be reasonably sure it's acting impartially.
The irony is that the black bias mentioned in this paper is probably due to inherent bias in image processing algorithms of the sensors themselves. Look up the "Shirley Card"
As for prompt engineering for gpt, it feels a bit like reading tea leaves. I'm not sure if it is possible to know for certain that a specific prompt will elicit the desire all the time.
So these models for now can be treated as a very reductive snapshot of humanity (at least English-speaking segment), warts and all
"Our model, despite being trained on a curated subset of the internet, still inherits its many unchecked biases and associations."
If these models find themselves into production environment - if they are good enough and profitable enough - they will eventually become legacy systems quietly perpetuating the biases of past times.