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

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. 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.

6 pages. Accepted at the 50th IEEE Computers, Software, and Applications Conference (COMPSAC 2026), Madrid, Spain, July 7-10, 2026

Artificial Intelligence (cs.AI)

arXiv:2608.14562 [cs.AI]

(or arXiv:2608.14562v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.14562 Focus to learn more arXiv-issued DOI via DataCite

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