What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems

• URL: http://arxiv.org/abs/2307.11784v1
• Date: Thu, 20 Jul 2023 12:40:55 GMT
• Title: What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety Critical Systems
• Authors: Saddek Bensalem, Chih-Hong Cheng, Wei Huang, Xiaowei Huang, Changshun Wu, Xingyu Zhao
• Abstract summary: Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. We first discuss the engineering and research challenges associated with the design and verification of such systems. Then, based on the observation that existing works cannot actually achieve provable guarantees, we promote a two-step verification method for the ultimate achievement of provable statistical guarantees.
• Score: 8.930000909500702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning has made remarkable advancements, but confidently utilising learning-enabled components in safety-critical domains still poses challenges. Among the challenges, it is known that a rigorous, yet practical, way of achieving safety guarantees is one of the most prominent. In this paper, we first discuss the engineering and research challenges associated with the design and verification of such systems. Then, based on the observation that existing works cannot actually achieve provable guarantees, we promote a two-step verification method for the ultimate achievement of provable statistical guarantees.

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