Who Defines the Boundary of Explanation? A Governance Philosophy of the Two-Layer Architecture in Human-AI Interaction

The AI industry is gradually weakening its white-box safety mechanisms. As models exhibit increasingly stable performance, engineers are asking a logically self-consistent question: if the model is right 99.9% of the time, why retain a rigid rule-based system to override it? This paper does not contest the engineering logic of this trend. It converges on a narrower but more lethal question: who defines the threshold? When the industry says "AI is good enough and no longer needs to be explained," who has the authority to make this judgment on behalf of the public? The paper distinguishes the necessity of post-hoc explanation (which can decline) from the possibility of process traceability (which must not be abandoned), and proposes a two-layer architecture: an output layer optimized for fluency, and a process-traceability layer that exists not to be used, but to exist. Its existence itself constitutes a constraint on power. Through the historical archetype of "Wang Mang" — trust completely established and then betrayed at the critical moment — the paper argues that any trust that can be built carries with it the possibility of being abused. The terminal proposition of explainability is not "how to make AI explainable," but "who has the authority to decide when AI no longer needs to be explained." AI工业界正在逐步弱化白盒安全机制。随着模型表现日益稳定,工程师提出了一个逻辑自洽的问题:如果模型在99.9%的情况下是对的,为什么还要保留笨重的规则系统来拦它?本文不争论这一趋势的工程逻辑。它收敛于一个更窄但更致命的问题:阈值由谁定义? 当工业界说"AI已经足够好,不再需要解释"时,谁有权替公众做这个判断?本文区分了事后解释的必要性(可以下降)与过程追溯的可能性(不应被放弃),提出双层架构:第一层是面向流畅性优化的输出层,第二层是过程追溯层——其核心功能不是"被使用",而是"始终存在"。它的存在本身就是对权力的一种约束。通过"王莽"这一历史原型——信任被完整建立之后,在关键时刻被滥用——本文论证:任何能被建立的信任,都同时存在被滥用的可能。可解释性的终极命题不是"如何让AI被解释",而是"谁有权决定AI何时不需要被解释"。