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

Akihisa Morikawa; Yutaka Matsubara

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arXiv · 2008.01263

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

Akihisa Morikawa·Yutaka Matsubara

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Abstract

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.

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Akihisa Morikawa, Yutaka Matsubara. 2020-08-04. Safety design concepts for statistical machine learning components toward accordance with functional safety standards. https://arxiv.org/abs/2008.01263

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