欧盟AI法案的技术标准框架，解决AI系统合规落地难题
Standards for trustworthy AI in the European Union: technical rationale, structural challenges, and an implementation path
- 构建分层标准体系：通用流程规范+行业特定阈值
- 提出可验证的稳定性检查、结构化文档与全生命周期保证案例
- 适合政策制定者、合规工程师和监管机构参考
本文探讨欧盟《人工智能法案》下AI标准化的技术基础。阐述统一标准如何支撑‘符合性推定’机制，介绍CEN/CENELEC标准化流程，并分析AI面临的独特挑战：随机行为、数据依赖性、评估实践不成熟及生命周期动态性。论文指出，AI系统通常是更大社会技术系统的组成部分，需采用分层方法：横向标准定义过程义务与证据结构，领域特定标准设定具体阈值与接受标准。提出基于风险管理的可行方案，包括可复现的技术检查（重新定义为测量属性的稳定性）、结构化文档、全面日志记录及随系统生命周期演进的保证案例。研究表明，尽管存在方法论困难，技术标准仍是将法律义务转化为可审计工程实践的关键，支持跨提供商、评估方和执法机构的规模化合规评估。
原文摘要 · Abstract (English)
This white paper examines the technical foundations of European AI standardization under the AI Act. It explains how harmonized standards enable the presumption of conformity mechanism, describes the CEN/CENELEC standardization process, and analyzes why AI poses unique standardization challenges including stochastic behavior, data dependencies, immature evaluation practices, and lifecycle dynamics. The paper argues that AI systems are typically components within larger sociotechnical systems, requiring a layered approach where horizontal standards define process obligations and evidence structures while sectoral profiles specify domain-specific thresholds and acceptance criteria. It proposes a workable scheme based on risk management, reproducible technical checks redefined as stability of measured properties, structured documentation, comprehensive logging, and assurance cases that evolve over the system lifecycle. The paper demonstrates that despite methodological difficulties, technical standards remain essential for translating legal obligations into auditable engineering practice and enabling scalable conformity assessment across providers, assessors, and enforcement authorities
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