当AI自主决策时，旧治理模式失效，需新机制保人类参与权。
Delegation Without Living Governance
- 提出运行时治理与'治理孪生'概念应对AI自主决策挑战
- 指出静态合规治理在AI实时决策下已失效
- 适合关注AI伦理、制度设计的政策与技术研究者
现有治理框架假设规则可预先设定、系统可被工程化合规，且责任可在事后追究。这一模式在机器替代体力劳动或加速计算时有效，但在判断力被委托给高速运行的智能体式AI时已不再适用。核心问题并非安全、效率或就业，而是人类是否仍能在日益塑造社会、经济与政治结果的系统中保持有意义的参与。本文认为，一旦决策进入运行时且变得不透明，静态合规型治理即告失效。更关键的是，问题不在于AI是否具备意识，而在于人类能否与日益异化的智能形式维持有效的沟通、影响力及共同演化。为此，本文提出运行时治理，特别是新提出的‘治理孪生’（Governance Twin）概念，作为维系人类相关性的有力候选方案，同时承认问责、代理权乃至惩罚机制必须在此转型中重新思考。
原文摘要 · Abstract (English)
Most governance frameworks assume that rules can be defined in advance, systems can be engineered to comply, and accountability can be applied after outcomes occur. This model worked when machines replaced physical labor or accelerated calculation. It no longer holds when judgment itself is delegated to agentic AI systems operating at machine speed. The central issue here is not safety, efficiency, or employment. It is whether humans remain relevant participants in systems that increasingly shape social, economic, and political outcomes. This paper argues that static, compliance-based governance fails once decision-making moves to runtime and becomes opaque. It further argues that the core challenge is not whether AI is conscious, but whether humans can maintain meaningful communication, influence, and co-evolution with increasingly alien forms of intelligence. We position runtime governance, specifically, a newly proposed concept called the Governance Twin [1]; as a strong candidate for preserving human relevance, while acknowledging that accountability, agency, and even punishment must be rethought in this transition.
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