让AI系统可审计，才能确保其合法合规
Can AI be Auditable?
- 从欧盟AI法案出发，构建AI全生命周期审计框架
- 指出技术黑箱、文档不统一等五大审计障碍
- 强调多方协作与标准化工具对落地审计的关键作用
审计能力是指人工智能系统在整个生命周期中被独立评估是否符合伦理、法律和技术标准的能力。本文探讨了审计能力如何通过新兴监管框架（如欧盟《人工智能法案》）逐步制度化，该法案要求系统性记录、风险评估和治理结构。文章分析了当前面临的多重挑战：技术不透明性、文档实践不一致、缺乏标准化审计工具与指标，以及现有负责任AI框架中的原则冲突。研究强调需制定清晰指南、推动国际规则协调，并建立坚实的社技融合方法论，以实现大规模审计。最后指出，必须通过多利益相关方合作与审计者赋权，构建有效的AI审计生态。审计能力应嵌入AI开发流程与治理体系，确保系统不仅可用，更合乎伦理与法律。
原文摘要 · Abstract (English)
Auditability is defined as the capacity of AI systems to be independently assessed for compliance with ethical, legal, and technical standards throughout their lifecycle. The chapter explores how auditability is being formalized through emerging regulatory frameworks, such as the EU AI Act, which mandate documentation, risk assessments, and governance structures. It analyzes the diverse challenges facing AI auditability, including technical opacity, inconsistent documentation practices, lack of standardized audit tools and metrics, and conflicting principles within existing responsible AI frameworks. The discussion highlights the need for clear guidelines, harmonized international regulations, and robust socio-technical methodologies to operationalize auditability at scale. The chapter concludes by emphasizing the importance of multi-stakeholder collaboration and auditor empowerment in building an effective AI audit ecosystem. It argues that auditability must be embedded in AI development practices and governance infrastructures to ensure that AI systems are not only functional but also ethically and legally aligned.
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