AI在关键领域落地难？这篇综述拆解了合规与创新的三大矛盾。
The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review
- 梳理2020-2025年文献，发现三类核心冲突：法规碎片化、中小企业负担重、治理模式不匹配。
- 提出风险分级监管、设计即合规、可解释AI等实用策略，支持可信部署。
- 适合政策制定者和企业技术负责人参考，推动监管与创新协同。
人工智能（AI）快速融入医疗、金融、能源和国防等关键基础设施，带来变革性效益，却与不断演进的监管与治理框架产生冲突。本文基于系统文献综述（SLR）方法，分析2020至2025年间发表的同行评审论文、报告及机构资料，揭示了三类相互关联的挑战：法规碎片化、中小型企业（SMEs）面临过重合规负担，以及治理模型错配。为应对这些挑战，研究提出风险分级监管、设计即合规（compliance by design）、可解释人工智能（explainable AI）等实践策略，以支持关键领域中可扩展且可信的AI部署。主要贡献包括对核心AI治理挑战的清晰映射、三类问题交叠的概念图示，以及面向政策制定者与从业者的可操作策略。
原文摘要 · Abstract (English)
The rapid integration of artificial intelligence (AI) into critical infrastructure including healthcare, finance, energy, and defense, offers transformative benefits but also conflicts with evolving regulatory and governance frameworks. This paper presents a systematic literature review (SLR) to examine the challenges of balancing AI compliance and technological innovation across critical infrastructure sectors. The review follows established SLR guidelines to extract and synthesize insights from peer-reviewed articles, report, and institutional sources published between 2020-2025. The study identifies three interrelated challenges: fragmented regulations, excessive compliance burdens for smaller to medium enterprises (SMEs), and misaligned governance models. To address these challenges, the study highlights practical governance strategies, including risk-tiered regulation, compliance by design, and explainable AI, to support scalable and trustworthy AI deployment in critical sectors. Key contributions include a concise mapping of core AI-governance challenges and a conceptual diagram illustrating their overlap, as well as actionable strategies for policymakers and practitioner to harmonize oversight with innovation.
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