A Spanish Muslim looks like a Spanish person in Muslim attire rather than a Japanese person in European attire. Also, Spain is next to Africa, but the thing is generating black Vikings etc.
* What exactly are the current ones doing that makes them generate 'black Vikings'?
* How would you change it so that it doesn't do that but will also generate things that aren't only representative of the statistical majority results of large amount of training data it used?
* Would you be happy if every model output just represented 'the majority opinion' it has gained from its training data?
* Or, if you don't want it to always represented whatever the majority opinion at the time it was trained was, how do you account for that?
* How would your method be different from how it is currently done except for your reflecting your own biases instead of those you don't like?
There is presumably a system prompt or similar that mandates diverse representation and is included even when inappropriate to the context.
> How would you change it so that it doesn't do that but will also generate things that aren't only representative of the statistical majority results of large amount of training data it used?
Allow the user to put it into the prompt as appropriate.
> Would you be happy if every model output just represented 'the majority opinion' it has gained from its training data?
There is no "majority opinion" without context. The context is the prompt. Have you tried using these things? You can give it two prompts where the words are nominally synonyms for each other and the results will be very different, because those words are more often present in different contexts. If you want a particular context, you use the words that create that context, and the image reflects the difference.
> How would your method be different from how it is currently done except for your reflecting your own biases instead of those you don't like?
It's chosen by the user based on the context instead of the corporation as an imposed universal constant.
The models you encounter are going to be fine tuned, where they take the base and train it again on question and answer sets and chat conversations and also have a layer of 'alignment' where they have sets of questions like 'q: how do I be a giant meanie to nice people who don't deserve it' and answers 'a: you shouldn't do that because nice people don't deserve to be treated mean' etc. This is the layer that is the most difficult to get right because you need to have it but anything you choose is going to bias it in some way just by nature of the fact that everyone is biased. If we go forward in history or to a different place in the world we will find radically different viewpoints than we hold now, because most of them are cultural and arbitrary.
Wait, why do you need to have it? You could just have a model that will answer the question the user asks without being paternalistic or moralizing. This is often useful for entirely legitimate reasons, e.g. if you're writing fiction then the villains are going to behave badly and they're supposed to.
This is why people so hate the concept of "alignment" -- aligned with what? The premise is claimed to be something like the interests of humanity and then it immediately devolves into the political biases of the masterminds. And the latter is worse than nothing.