UK Study Identifies Major Gaps Between National AI Policy and Local Implementation in Public Services
A systematic literature review published on arXiv identifies three core challenges hindering effective AI governance in critical sectors: fragmented regulations, excessive compliance burdens on small and medium enterprises, and misaligned governance models. The review synthesizes peer-reviewed articles and institutional sources from 2020–2025 covering AI deployment in healthcare, finance, energy, and defense. The findings matter because they offer actionable strategies—including risk-tiered regulation and explainable AI—to help policymakers and practitioners reconcile oversight with technological innovation.
A preprint submitted to arXiv and accepted at the Hawaii International Conference on System Sciences presents a systematic literature review (SLR) examining how AI integration into critical infrastructure conflicts with existing and emerging regulatory frameworks. Drawing on peer-reviewed articles and institutional sources published between 2020 and 2025, the authors identify three interrelated governance challenges: regulatory fragmentation across jurisdictions and sectors, disproportionate compliance burdens on smaller enterprises, and governance models that are misaligned with the pace of AI development. The paper maps these challenges conceptually and proposes practical mitigation strategies, including risk-tiered regulation that scales requirements to potential harm, compliance-by-design approaches embedded in development workflows, and explainable AI techniques to improve transparency and accountability. The authors argue that without coordinated governance reform, smaller innovators may be effectively excluded from critical sectors while larger incumbents absorb compliance costs more easily. The study is intended to inform both policymakers seeking to update regulatory frameworks and practitioners navigating current compliance requirements.
What's missing
As a literature review, the paper inherits the limitations of its source corpus, including potential publication bias toward studies from well-resourced institutions and English-language outlets. The review does not appear to include empirical primary data or case studies measuring actual compliance costs, which limits the strength of its policy recommendations. The geographic scope of the reviewed literature is not specified, raising questions about whether findings generalize across different regulatory environments such as the EU AI Act versus US sector-specific frameworks.
What different sources said
- arXiv cs.AICenter
The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review
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