"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.