Global AI Regulations for FAIR and Ethics in High-Risk Use Cases: A Comparative Review
06:00 · August 18, 2026 · arXiv cs.AI RSS
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.
Summary
AI governance is moving from voluntary ethical guidelines toward enforceable, risk-based rules, yet differences among the EU, US, and China create compliance uncertainty for operators of high-stakes systems. A comparative matrix maps how each jurisdiction classifies high-risk AI, imposes binding obligations across the lifecycle, enforces accountability, and operationalises the FAIR principles of findability, accessibility, interoperability, and reusability. The analysis draws on primary legal texts to reveal recurring weaknesses: limited interoperability requirements, overlapping obligations from AI, sector, and data-protection rules, and incomplete governance for critical digital infrastructure.
The matrix is tested against three concrete high-risk domains. The first examines EEG-guided robotic neurorehabilitation, where biometric health data triggers concurrent medical-device, data-protection, and AI-specific duties. The second considers AI-enabled debt collection in prospective central-bank digital currency systems, highlighting fairness and due-process risks when automated decisions affect credit access or payment constraints. The third addresses AI-driven allocation of scarce GPU resources in emerging AI-factory infrastructures, a setting where high-performance computing governance remains largely unspecified despite clear societal impact.
Across these cases the authors identify three persistent gaps. Interoperability mandates are weak, cross-regime obligations prove difficult to reconcile in practice, and oversight of critical digital infrastructure is under-specified. To address these shortcomings they outline Knowledge Blocks: modular, machine-checkable compliance artefacts expressed in RDF/OWL for representation, SHACL for validation, and PROV-O for provenance tracking. The approach supports automated conformity checks and audit-ready evidence graphs that can span multiple regulatory regimes without requiring manual re-documentation for each jurisdiction.
Why it matters
Directly addresses EU AI Act implementation gaps relevant to Dutch enterprises and researchers; offers actionable compliance patterns and identifies regulatory blind spots for high-risk AI deployment in the Netherlands/EU context.
