arXiv 2603.269832026-03-27cs.AIcs.CYcs.LG

透明度作为架构：欧盟AI法案第50条II项中的结构性合规缺口

Transparency as Architecture: Structural Compliance Gaps in EU AI Act Article 50 II

Technichal University Berlin XpaliNLP Group（柏林工业大学 XpaliNLP 组）；Research Center for Artificial Intelligence (DFKI)（人工智能研究中心（DFKI））；Center for European Research on Trusted AI (CERTAIN)（欧洲可信人工智能研究中心（CERTAIN））

Vera Schmitt, Niklas Kruse, Premtim Sahitaj, Julius Schöning

备注：: 10 pages, 2 figures
DOI：: 10.63317/5hwpjzkvb4wf

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AI总结：

本文探讨了欧盟AI法案第50条II项对AI生成内容的双重透明度要求，指出当前生成式AI系统存在结构性合规缺口，需将透明度视为架构设计需求。

摘要：

欧盟人工智能法案第50条II项要求AI生成内容的输出必须以可被人类理解和机器读取的形式进行标注，以供自动化验证。这一要求将于2026年8月生效，但与当前生成式AI系统的根本限制相冲突。通过合成数据生成和自动事实核查作为诊断用例，本文表明合规性不能仅通过事后标注来实现。在事实核查流程中，溯源跟踪在迭代编辑流程和非确定性LLM输出下不可行；此外，辅助功能豁免不适用，因为此类系统主动分配真值而非支持编辑呈现。在合成数据生成中，持续的双模式标注是矛盾的：水印在经过人类审查后可能在训练过程中被学习为虚假特征，而适合机器验证的标记则在标准数据处理下容易损坏。在两个领域中，三个结构性缺口阻碍了合规性：（a）缺乏跨平台标注格式用于交错的人工-AI输出；（b）法规中“可靠性”标准与概率模型行为之间的不一致；（c）缺乏指导以适应异构用户专业知识的披露调整。关闭这些缺口需要将透明度视为架构设计要求，需要跨法律语义、AI工程和以人为本的设计进行跨学科研究。

英文摘要：

Art. 50 II of the EU Artificial Intelligence Act mandates dual transparency for AI-generated content: outputs must be labeled in both human-understandable and machine-readable form for automated verification. This requirement, entering into force in August 2026, collides with fundamental constraints of current generative AI systems. Using synthetic data generation and automated fact-checking as diagnostic use cases, we show that compliance cannot be reduced to post-hoc labeling. In fact-checking pipelines, provenance tracking is not feasible under iterative editorial workflows and non-deterministic LLM outputs; moreover, the assistive-function exemption does not apply, as such systems actively assign truth values rather than supporting editorial presentation. In synthetic data generation, persistent dual-mode marking is paradoxical: watermarks surviving human inspection risk being learned as spurious features during training, while marks suited for machine verification are fragile under standard data processing. Across both domains, three structural gaps obstruct compliance: (a) absent cross-platform marking formats for interleaved human-AI outputs; (b) misalignment between the regulation's 'reliability' criterion and probabilistic model behavior; and (c) missing guidance for adapting disclosures to heterogeneous user expertise. Closing these gaps requires transparency to be treated as an architectural design requirement, demanding interdisciplinary research across legal semantics, AI engineering, and human-centered desi

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