提出AI安全风险地图方法，系统识别并证明无人列车系统无安全隐患。
Landscape of AI safety concerns -- A methodology to support safety assurance for AI-based autonomous systems
- 构建安全风险地图，系统化识别AI特有安全隐患
- 通过无人区域列车案例验证方法可行性，支持安全论证
- 适合需合规认证的自动驾驶系统研发与评审人员
人工智能已成为推动多领域发展的关键技术，其在现代自主系统中的集成要求保障安全性。然而，包含AI组件的系统安全保证面临巨大挑战：缺乏明确规范、运行环境与系统本身复杂性高，导致行为不确定性加剧，难以形成令人信服的安全证据。为此，学界建议深入分析并缓解特定于AI的不足，即所谓AI安全关切，从而为有力的安全论证提供关键证据。本文在此基础上提出「AI安全风险地图」这一新方法，旨在通过系统性地证明不存在AI安全关切，支持面向基于AI系统的安全保证案例构建。该方法在一辆无人驾驶区域列车的案例研究中得到应用，展示了其实际可行性和有效性。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) has emerged as a key technology, driving advancements across a range of applications. Its integration into modern autonomous systems requires assuring safety. However, the challenge of assuring safety in systems that incorporate AI components is substantial. The lack of concrete specifications, and also the complexity of both the operational environment and the system itself, leads to various aspects of uncertain behavior and complicates the derivation of convincing evidence for system safety. Nonetheless, scholars proposed to thoroughly analyze and mitigate AI-specific insufficiencies, so-called AI safety concerns, which yields essential evidence supporting a convincing assurance case. In this paper, we build upon this idea and propose the so-called Landscape of AI Safety Concerns, a novel methodology designed to support the creation of safety assurance cases for AI-based systems by systematically demonstrating the absence of AI safety concerns. The methodology's application is illustrated through a case study involving a driverless regional train, demonstrating its practicality and effectiveness.
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