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In recent years, curial incidents and accidents have been reported due toun-intended control caused by misjudgment of statistical machine learning(SML), which include deep learning. The international functional safetystandards for Electric/Electronic/Programmable (E/E/P) systems have been widelyspread to improve safety. However, most of them do not recom-mended to use SMLin safety critical systems so far. In practical the new concepts and methodsare urgently required to enable SML to be safely used in safety criticalsystems. 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 developmentprocess based on XAI (eXplainable Artificial Intelligence) and Automotive SPICEto improve explainability and reliability in development phase. Fi-nally, webriefly compare the TSCs in cost and difficulty, and expect to en-couragefurther discussion in many communities and domain.

Safety design concepts for statistical machine learning components toward accordance with functional safety standards