论文标题
统计机器学习组件的安全设计概念符合功能安全标准
Safety design concepts for statistical machine learning components toward accordance with functional safety standards
论文作者
论文摘要
近年来，由于统计机器学习的错误判断（SML）引起的，包括深度学习，汇总事件和事故已报道。电力/电子/可编程（E/E/P）系统的国际功能安全标准已广泛扩展以提高安全性。但是，到目前为止，他们中的大多数人都没有建议在安全关键系统中使用SML。实际上，迫切需要新的概念和方法，以使SML能够安全地用于安全关键系统。在本文中，我们组织了五种技术安全概念（TSC），以符合功能安全标准。我们不仅讨论了定量评估标准，还讨论了基于XAI（可解释的人工智能）和汽车香料的开发过程，以提高开发阶段的解释性和可靠性。 fi-nally，我们将成本和难度简要比较TSC，并期望在许多社区和领域进行进一步的讨论。
In recent years, curial incidents and accidents have been reported due to un-intended control caused by misjudgment of statistical machine learning (SML), which include deep learning. The international functional safety standards for Electric/Electronic/Programmable (E/E/P) systems have been widely spread to improve safety. However, most of them do not recom-mended to use SML in safety critical systems so far. In practical the new concepts and methods are urgently required to enable SML to be safely used in safety critical systems. In this paper, we organize five kinds of technical safety concepts (TSCs) for SML components toward accordance with functional safety standards. We discuss not only quantitative evaluation criteria, but also development process based on XAI (eXplainable Artificial Intelligence) and Automotive SPICE to improve explainability and reliability in development phase. Fi-nally, we briefly compare the TSCs in cost and difficulty, and expect to en-courage further discussion in many communities and domain.
