为帮助从业者准确分类AI系统风险等级，研究测试了自服务工具的实际效果。
Self-Service or Not? How to Guide Practitioners in Classifying AI Systems Under the EU AI Act
- 基于设计科学方法，开发并测试了在线决策支持工具。
- 78名跨领域从业者参与，发现法律定义理解困难是主要障碍。
- 清晰解释与实例能显著提升分类准确性，适合政策制定者和工具开发者参考。
2024年8月，欧盟人工智能法案（AIA）正式实施，成为全球首个大规模人工智能监管框架。其核心是基于风险的治理模式，将监管责任与AI系统的潜在危害相匹配。为此，AIA制定了风险分类体系（RCS），将AI系统分为四个风险等级。尽管该体系符合风险导向监管的理论基础，但实际应用复杂，需融合法律、技术与领域知识。尽管学术讨论日益增多，针对从业者如何在真实场景中应用RCS的实证研究仍寥寥无几。本研究通过设计科学方法，利用自服务网络工具评估78名来自不同领域的工业从业者对RCS的应用情况。研究揭示了法律定义解读和监管范围界定中的关键挑战，并表明针对性支持（如清晰说明与实用案例）可显著改善分类过程。研究成果为工具设计者与政策制定者提供了切实可行的实践建议，以推动AIA合规落地。
原文摘要 · Abstract (English)
In August 2024, the EU Artificial Intelligence Act (AIA) came into force, marking the world's first large-scale regulatory framework for AI. Central to the AIA is a risk-based approach, aligning regulatory obligations with the potential harm posed by AI systems. To operationalize this, the AIA defines a Risk Classification Scheme (RCS), categorizing systems into four levels of risk. While this aligns with the theoretical foundations of risk-based regulations, the practical application of the RCS is complex and requires expertise across legal, technical, and domain-specific areas. Despite increasing academic discussion, little empirical research has explored how practitioners apply the RCS in real-world contexts. This study addresses this gap by evaluating how industrial practitioners apply the RCS using a self-service, web-based decision-support tool. Following a Design Science Research (DSR) approach, two evaluation phases involving 78 practitioners across diverse domains were conducted. Our findings highlight critical challenges in interpreting legal definitions and regulatory scope, and show that targeted support, such as clear explanations and practical examples, can significantly enhance the risk classification process. The study provides actionable insights for tool designers and policymakers aiming to support AIA compliance in practice.
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