Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review

全球高风险人工智能应用场景下的 FAIR 原则与伦理监管：比较综述

Abstract: 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.

摘要： 人工智能治理正从自愿性伦理转向可执行的、基于风险的监管，然而跨司法管辖区的差异为高风险人工智能的运营者带来了合规不确定性。我们提出了一个针对欧盟、美国和中国的比较矩阵，旨在映射：(i) 风险分类触发条件，(ii) 约束性义务，(iii) 执行与问责机制，以及 (iv) FAIR 原则在实践中的操作化程度。

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.

我们选取了三个高影响力的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、未来央行数字货币（CBDC）生态系统中的人工智能催收，以及新兴人工智能工厂基础设施中稀缺图形处理器（GPU）资源的人工智能驱动分配。

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.

通过分析原始法律文本和实施证据，我们识别出三个反复出现的问题：互操作性要求薄弱、跨制度义务（人工智能 + 行业监管 + 数据保护）的操作化困难，以及针对关键数字基础设施应用场景的治理规范不足。

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.

为了弥合实施差距，我们概述了“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）的机器可校验合规工件模式，旨在实现跨多种制度的、审计就绪的“合规即设计”（compliance-by-design）。