高风险用例中面向FAIR原则与伦理的全球AI监管：比较综述
Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review
AI总结：
本文通过比较欧盟、美国、中国的AI监管框架，分析高风险AI用例的合规缺口，提出基于RDF/OWL等技术的知识块模式以实现跨制度设计型合规。
AI中文摘要：
AI治理正从自愿性伦理转向可执行的、基于风险的监管，但司法管辖区间的差异给高风险AI的运营者带来合规不确定性。本文提出了欧盟、美国和中国的比较矩阵，该矩阵涵盖四个维度：（i）风险分类触发条件，（ii）具有约束力的义务，（iii）执行与问责机制，（iv）FAIR原则在实践中的落实程度。我们在三个高影响力领域对该矩阵进行了压力测试：脑电图（EEG）引导的康复机器人、前瞻性央行数字货币（CBDC）生态系统中基于AI的债务催收，以及新兴AI工厂基础设施中AI驱动的稀缺图形处理器（GPU）资源分配。通过使用一手法律文本和实施证据，我们发现三个反复出现的缺口：互操作性要求薄弱、跨制度义务（AI+行业监管+数据保护）难以落实，以及关键数字基础设施用例的治理规定不足。为弥合实施缺口，我们提出了知识块（Knowledge Blocks），这是一种基于资源描述框架/网络本体语言（RDF/OWL）、形状约束语言（SHACL）和溯源本体（PROV-O）的机器可核查合规人工制品模式，可在多个制度间实现可审计的设计型合规。
英文摘要：
AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI. We present a comparative matrix for the EU, US, and China that maps (i) risk classification triggers, (ii) binding obligations, (iii) enforcement and accountability mechanisms, and (iv) the degree to which FAIR principles are operationalised in practice. We stress-test the matrix on three high-impact domains: Electroencephalography (EEG)-guided rehabilitation robotics, AI-enabled debt collection in prospective Central Bank Digital Currency (CBDC) ecosystems, and AI-driven allocation of scarce Graphics Processing Unit (GPU) resources in emerging AI Factory infrastructures. Using primary legal texts and implementation evidence, we identify three recurring gaps: weak interoperability mandates, difficult operationalisation of cross-regime obligations (AI + sector regulation + data protection), and under-specified governance for critical digital infrastructure use cases. To bridge the implementation gap, we outline Knowledge Blocks, a machine-checkable compliance artefact pattern based on Resource Description Framework/Web Ontology Language (RDF/OWL), Shapes Constraint Language (SHACL), and Provenance Ontology (PROV-O), enabling audit-ready compliance-by-design across multiple regimes.
