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Locks for the use of IEC 61508 to ML Safety-Critical Applications and Possible Solutions

Abstract : In the past few years, Artificial Intelligence (AI) and more precisely Machine Learning (ML), have been developing quickly and have found applications in a lot of various fields, including industry. The process industry and the use of machinery are critical because they represent a high risk for the surroundings, humans, goods and environment. To mitigate those risks, safety devices carrying out safety functions are used to monitor and control processes and machinery in real time. IEC 61508 which gives a framework to certify the Safety Integrity Level (SIL) of such systems is not suitable for complex algorithms like ML algorithms can be. We will attempt in this paper to discuss the general concepts of the standard that hinder the development and assessment of a safety devices using AI in the current state of knowledge. To the end, after describing the specificities of safety-critical systems and presenting the basic principles of AI and ML, this paper highlights the locks for the use of IEC 61508 and suggests some leverages, such as the use of a new safety lifecycle, a minimal level of transparency and explainability as well as the demonstration of robustness based on formal verification, testing and new metrics. Disclaimer: AI is an area of research which is currently booming, the scientific knowledge is progressing rapidly, especially on the topics of safety assurance, testing, V&V, robustness, explainable AI (XAI). Thus, this article could not be exhaustive and represents our current state of knowledge on that matter. Some information in this paper have been taken from standards projects of the ISO/IEC JTC 1 SC42.
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Submitted on : Wednesday, December 22, 2021 - 10:48:13 AM
Last modification on : Wednesday, December 22, 2021 - 11:33:52 AM

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Albin Tarrisse, François Massé. Locks for the use of IEC 61508 to ML Safety-Critical Applications and Possible Solutions. Proceedings of the 31st European Safety and Reliability Conference, Sep 2021, Angers, France. pp.3459-3466, ⟨10.3850/978-981-18-2016-8_661-cd⟩. ⟨ineris-03500334⟩

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