"woke" isn't a great qualifier. But you could re-frame the question as how do "AI" models encode and search within a political sentiment space? Here's some political spaces, usually in 4, 6, 9 and 12 dimensional variants [0..2] There are also personality axes, again with high or low dimensional nuance. Unlike "real" signals there's no component analysis to prove that these are orthogonal. If the training data carries-in any salient features you can get an NN to tell you where "woke" or "fascist" or whatever is within these for some task, then minimise or maximise it for some quality.

[0] https://www.thebehavioralscientist.com/glossary/political-co...

[1] https://politicaltests.github.io/12axes/

[2] https://9axes.github.io/

[3] https://en.wikipedia.org/wiki/Personality