> is that the technical challenges lie in finding ways to organise and construct language and image datasets in a way that is as ‘neutral’ as possible I think

> is that the technical challenges lie in finding ways to organise and construct language and image datasets in a way that is as ‘neutral’ as possible I think part of the problem is that 'neutral' models dont exist. And framing it that way already loses half the battle. There is no such thing as a neutral perspective. It's like when you learn a language, people always say they want to lose their accent as if it's a process of reduction to some perfect median. When in truth the way to 'lose your accent' is to pick a model speaker you like and mimic theirs. Of course I do agree we should probably replace the current biased ones with better ones, but the 'neutral' goal irks me. When it is picking one bias for another.