梳理AI治理核心原则，揭示框架与实践的差距。
Toward Effective AI Governance: A Review of Principles
- 综述9篇顶会文献，提炼主流治理框架与原则
- 透明与问责最受关注，但具体执行机制少有提及
- 适合政策制定者与企业AI合规团队参考
人工智能治理是建立框架、政策与流程以确保AI系统负责任、合乎伦理且安全开发与部署的实践。尽管治理是负责任AI的核心支柱，现有文献仍缺乏对各类治理框架与实践的整合分析。本文通过快速三级综述方法，筛选2020至2024年发表于IEEE和ACM的九篇同行评审二次研究，采用结构化纳入标准与主题语义合成。结果表明，被引用最多的框架包括欧盟AI法案（EU AI Act）和NIST风险管理框架（NIST RMF），最常出现的原则为透明性与问责制。然而，多数综述未详细描述可操作的治理机制或利益相关方策略。结论指出，该综述整合了当前AI治理的关键方向，同时揭示了实证验证不足与包容性欠缺的缺口。研究发现可为学术探索及组织实践提供支持。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) governance is the practice of establishing frameworks, policies, and procedures to ensure the responsible, ethical, and safe development and deployment of AI systems. Although AI governance is a core pillar of Responsible AI, current literature still lacks synthesis across such governance frameworks and practices. Objective: To identify which frameworks, principles, mechanisms, and stakeholder roles are emphasized in secondary literature on AI governance. Method: We conducted a rapid tertiary review of nine peer-reviewed secondary studies from IEEE and ACM (20202024), using structured inclusion criteria and thematic semantic synthesis. Results: The most cited frameworks include the EU AI Act and NIST RMF; transparency and accountability are the most common principles. Few reviews detail actionable governance mechanisms or stakeholder strategies. Conclusion: The review consolidates key directions in AI governance and highlights gaps in empirical validation and inclusivity. Findings inform both academic inquiry and practical adoption in organizations.
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