From Legal Text to AI-specific Risk Sources: A Systematic Analysis of the EU AI Act's High-Risk Requirements

📅 2026-09-11
📈 Citations: 0
Influential: 0
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🤖 AI Summary
论文通过系统分析欧盟AI法案的高风险要求,将其与现有AI风险分类进行对比,旨在为AI风险管理提供结构化参考。
📝 Abstract
The EU AI Act introduces mandatory requirements for high-risk AI systems with the explicit goal of ensuring the development and operation of trustworthy AI. At the same time, AI risk management practices rely on structured risk taxonomies to systematically identify and treat AI-specific risk sources. As both the AI Act and established risk taxonomies aim to address AI-induced risks, a natural question is whether they align in the risk sources they cover. However, no clear mapping exists between the risks implicitly addressed by the Act's high-risk requirements and established taxonomies, leaving practitioners without a structured basis for aligning regulatory obligations with AI risk management practice. This paper presents a systematic classification of the requirements extracted from the EU AI Act Section 2 (Requirements for high-risk AI systems), revealing that only a minority directly address AI-specific risk sources, while the majority impose organizational process and documentation obligations. From the AI risk-related requirements, a consolidated list of distinct AI-specific risk sources is derived. The resulting EU AI Act Risk Source List takes an important step towards bridging the gap between legal obligation and AI risk management practice, providing a structured reference for explicit comparison between existing AI risk taxonomies and the risk sources implicitly addressed by the EU AI Act. Important Note: This is the authors' preprint. The paper was presented at the 4th International Conference on Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications. A link to the conference's official proceedings will be provided upon publication.
Problem

Research questions and friction points this paper is trying to address.

EU AI Act
high-risk AI systems
risk taxonomies
regulatory obligations
AI risk management
Innovation

Methods, ideas, or system contributions that make the work stand out.

systematic classification
AI-specific risk sources
EU AI Act
risk management practice
structured reference
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Ronald Schnitzer
Technical University of Munich, School of Computation, Information and Technology, Munich, Germany; Siemens AG, Munich/Vienna, Germany/Austria
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Mike Auer
Siemens AG, Munich/Vienna, Germany/Austria
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Rumpa Choudhury
Siemens AG, Munich/Vienna, Germany/Austria
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Andreas Hapfelmeier
Siemens AG, Munich/Vienna, Germany/Austria
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Maximilian Hoeving
Siemens AG, Munich/Vienna, Germany/Austria
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Isabelle Painter
Siemens AG, Munich/Vienna, Germany/Austria
Josiane Xavier Parreira
Josiane Xavier Parreira
Siemens AG, Munich/Vienna, Germany/Austria
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Sonja Zillner
Technical University of Munich, School of Computation, Information and Technology, Munich, Germany; Siemens AG, Munich/Vienna, Germany/Austria