人工智能治理的五层框架：整合监管、标准与认证
A five-layer framework for AI governance: integrating regulation, standards, and certification
AI总结：
本文提出一个五层AI治理框架，通过整合监管指令、标准与认证流程，连接监管原则与实际实施，并经案例验证能有效识别合规差距，为政策制定者和行业提供可行的风险管理路线图。
摘要：
目的：人工智能（AI）系统的治理需要一种结构化的方法，将高层监管原则与实际实施联系起来。现有框架在法规如何转化为合规机制方面缺乏明确性，导致合规与执行存在差距。本文旨在解决AI治理中的这一关键差距。方法/途径：提出了一个五层AI治理框架，从广泛的监管指令到具体标准、评估方法和认证流程。通过逐层聚焦缩小范围，该框架提供了一条满足技术、监管和伦理要求的结构化路径。其适用性通过AI公平性和AI事件报告的两个案例研究得到验证。发现：案例研究证明了该框架识别法律指令、标准化和实施中差距的能力。它适应全球和特定区域的AI治理需求，将监管指令与实际应用映射，以改善合规性和风险管理。实际意义：通过提供清晰可行的路线图，这项工作为政策制定者、监管机构和行业利益相关者提供了加强合规和风险管理的模型，为全球AI治理做出贡献。社会意义：该框架支持制定建立公众信任并促进AI伦理使用以造福社会的政策。原创性/价值：本研究提出了一个连接高层监管指令和实施指南的五层AI治理框架。通过AI公平性和事件报告的案例研究验证，它识别了诸如缺失标准化评估程序和报告机制等差距，为有针对性的治理措施提供了结构化基础。
英文摘要：
Purpose: The governance of artificial iintelligence (AI) systems requires a structured approach that connects high-level regulatory principles with practical implementation. Existing frameworks lack clarity on how regulations translate into conformity mechanisms, leading to gaps in compliance and enforcement. This paper addresses this critical gap in AI governance. Methodology/Approach: A five-layer AI governance framework is proposed, spanning from broad regulatory mandates to specific standards, assessment methodologies, and certification processes. By narrowing its scope through progressively focused layers, the framework provides a structured pathway to meet technical, regulatory, and ethical requirements. Its applicability is validated through two case studies on AI fairness and AI incident reporting. Findings: The case studies demonstrate the framework's ability to identify gaps in legal mandates, standardization, and implementation. It adapts to both global and region-specific AI governance needs, mapping regulatory mandates with practical applications to improve compliance and risk management. Practical Implications - By offering a clear and actionable roadmap, this work contributes to global AI governance by equipping policymakers, regulators, and industry stakeholders with a model to enhance compliance and risk management. Social Implications: The framework supports the development of policies that build public trust and promote the ethical use of AI for the benefit of society. Originality/Value: This study proposes a five-layer AI governance framework that bridges high-level regulatory mandates and implementation guidelines. Validated through case studies on AI fairness and incident reporting, it identifies gaps such as missing standardized assessment procedures and reporting mechanisms, providing a structured foundation for targeted governance measures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
