[danaris]

(Note that I'm not attempting to contradict you in any way—your comment merely raised this issue, which I think is worth commenting on explicitly.)

One thing that various industries are currently coming to terms with is the fact that it is effectively impossible to create "neutrality".

Every one of your choices of what to include in the training data, what to explicitly exclude, what to try to emphasize, and any other ways you prune or shape a model, make it conform to one set of biases or another.

"Unaligned," here, at least as far as I can tell, is just a shorthand for "a model no one explicitly went in after training and pushed to do one thing or not do another." It doesn't mean that the model is unbiased...because even if your model contains absolutely everything in human knowledge, with no aspect being disproportionate to reality, real humans are also biased, and that "model replicating reality" is just going to replicate those real biases too.

It's always going to be more effective to acknowledge our own biases, both to ourselves and our audiences (whatever those may look like), and when we do try to shape something like a model, simply be honest about what that shaping looks like.