As AI assistants become integrated into safety engineering workflows for Physical AI systems, a critical question emerges: does AI assistance improve safety analysis quality, or introduce systematic blind spots that surface only through post-deployment incidents? This paper develops a formal framework for AI assistance in safety analysis. We first establish why safety engineering resists benchmark-driven evaluation: safety competence is irreducibly multidimensional, constrained by context-dependent correctness, inherent incompleteness, and legitimate expert disagreement. We formalize this through a five-dimensional competence framework capturing domain knowledge, standards expertise, operational experience, contextual understanding, and judgment. We introduce the competence shadow: the systematic narrowing of human reasoning induced by AI-generated safety analysis. The shadow is not what the AI presents, but what it prevents from being considered. We formalize four canonical human-AI collaboration structures and derive closed-form performance bounds, demonstrating that the competence shadow compounds multiplicatively to produce degradation far exceeding naive additive estimates. The central finding is that AI assistance in safety engineering is a collaboration design problem, not a software procurement decision. The same tool degrades or improves analysis quality depending entirely on how it is used. We derive non-degradation conditions for shadow-resistant workflows and call for a shift from tool qualification toward workflow qualification for trustworthy Physical AI.

翻译：随着人工智能助手融入物理AI系统的安全工程工作流，一个关键问题浮现：AI辅助是提升了安全分析质量，还是引入了系统性盲区，这些盲区仅在部署后的事故中才显露？本文构建了安全分析中AI辅助的形式化框架。我们首先阐明安全工程为何抗拒基准驱动评估：安全能力具有不可约的多维性，受限于上下文相关的正确性、固有不完备性以及领域专家的合理分歧。我们通过五维能力框架对此进行形式化，涵盖领域知识、标准专长、操作经验、情境理解与判断力。我们提出"能力盲区"概念：AI生成的安全分析导致的人类推理系统性收窄。盲区并非AI呈现的内容，而是它阻止人类考虑的内容。我们形式化了四种典型的人机协作结构，并推导出闭式性能边界，证明能力盲区会以乘法方式复合累积，产生远超朴素加法估计的性能退化。核心发现是：安全工程中的AI辅助本质上是协作设计问题，而非软件采购决策。同一工具对分析质量的提升或削弱完全取决于其使用方式。我们推导了抗盲区工作流的非退化条件，并呼吁从工具资格认证转向工作流资格认证，以实现可信赖的物理AI系统。