从义务到规范：关于在需求工程中验证欧盟人工智能法案要求的调查

中文摘要 AI 辅助

随着欧盟人工智能法案生效，开发或运营人工智能系统的组织在透明度、风险管理和可追溯性上面临新义务。对于需求工程而言，这些义务须转化为可测试、可审计的要求及可验证的证据，但许多组织目前缺乏实现此目标的系统流程。我们假设基于大语言模型的智能验证工具可助力这一转化。为此开展了一项混合方法探索性研究，包括专家访谈（10人）和在线调查（15人），以评估组织对面向欧盟人工智能法案的需求工程的准备情况以及对基于大语言模型的智能闭环验证工具的看法。结果表明，尽管欧盟人工智能法案被视为高度相关，但往往缺乏捕捉监管义务、将更新传播到项目中以及维持全生命周期可追溯性和证据的结构化机制。参与者认为基于大语言模型的工具在将义务映射到要求、评估覆盖范围和组织证据方面很有前景，但对完全自动化表达了强烈担忧并强调需要保障措施。基于这些发现，我们概述了适用于欧盟人工智能法案的闭环方法的最低要求。

英文摘要

With the EU AI Act entering into force, organizations developing or operating AI systems face new obligations on transparency, risk management, and traceability. For Requirements Engineering (RE), these obligations must be translated into testable, auditable requirements and verifiable evidence. However, many organizations currently lack systematic processes to achieve this. We hypothesize that LLM-based agentic validation tools can support this translation, thereby helping to close this gap. We present a mixed-method exploratory study with expert interviews (N=10) and an online survey (N=15) to assess organizational preparedness for EU AI Act-oriented RE and perceptions of LLM-based, agentic closed-loop validation tools, with participants spanning RE, data science, development, and compliance roles. Our results show that, although the EU AI Act is viewed as highly relevant, structured mechanisms to capture regulatory obligations, propagate updates into projects, and maintain lifecycle-wide traceability and evidence are often missing. Participants see LLM-based tools as promising for mapping obligations to requirements, assessing coverage, and organizing evidence, but express strong concerns about full automation and stress the need for safeguards. Based on these findings, we outline minimum requirements for an EU AI Act-ready closed-loop approach.

发表机构

• Fraunhofer IAIS（弗劳恩霍夫智能分析与信息系统研究所）
• T-Systems International GmbH（T系统国际有限公司）
• Fraunhofer IESE（弗劳恩霍夫实验软件工程研究所）

机构由 AI 辅助整理，请以论文原文为准。