On (1), models are an aggregation of large volumes of data sources, whatever the culture producing them, you'll get the average bias of that culture.

We see that on what minorities are associated with inside the model, or how things that aren't online will have a completely different weight. Or how 2/4/5/8ch or X will be disproportionately present in specific models despite being the places where facts go to die.

I see your point, but this isn't exactly true, it only takes a single person to bias a model by deleting specific training data or over training on certain facts.