Modern artificial intelligence governance lacks a formal, enforceable mechanism for determining whether a given AI system is legally permitted to operate in a specific domain and jurisdiction. Existing tools such as model cards, audits, and benchmark evaluations provide descriptive information about model behavior and training data but do not produce binding deployment decisions with legal or financial force. This paper introduces the AI Deployment Authorisation Score (ADAS), a machine-readable regulatory framework that evaluates AI systems across five legally and economically grounded dimensions: risk, alignment, externality, control, and auditability. ADAS produces a cryptographically verifiable deployment certificate that regulators, insurers, and infrastructure operators can consume as a license to operate, using public-key verification and transparency mechanisms adapted from secure software supply chain and certificate transparency systems. The paper presents the formal specification, decision logic, evidence model, and policy architecture of ADAS and demonstrates how it operationalizes the European Union Artificial Intelligence Act, United States critical infrastructure governance, and insurance underwriting requirements by compiling statutory and regulatory obligations into machine-executable deployment gates. We argue that deployment-level authorization, rather than model-level evaluation, constitutes the missing institutional layer required for safe, lawful, and economically scalable artificial intelligence.

翻译：现代人工智能治理缺乏一种正式且可执行的机制，用以判定特定人工智能系统在特定领域和司法管辖区内是否被法律允许运行。现有的工具，如模型卡片、审计和基准评估，提供了关于模型行为和训练数据的描述性信息，但无法产生具有法律或财务约束力的部署决策。本文提出了人工智能部署授权评分（ADAS），这是一个机器可读的监管框架，从五个基于法律和经济的维度评估人工智能系统：风险、对齐性、外部性、可控性和可审计性。ADAS生成一个可加密验证的部署证书，监管机构、保险公司和基础设施运营商可以将其作为运营许可证使用，该证书采用了从安全软件供应链和证书透明度系统借鉴的公钥验证与透明度机制。本文阐述了ADAS的形式化规范、决策逻辑、证据模型和政策架构，并展示了其如何通过将法定和监管义务编译成机器可执行的部署关卡，来具体实施欧盟《人工智能法案》、美国关键基础设施治理以及保险承保要求。我们认为，部署层面的授权，而非模型层面的评估，构成了安全、合法且经济上可扩展的人工智能所缺失的制度层。