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Improving ML Safety with Partial Specifications

  • Rick SalayEmail author
  • Krzysztof Czarnecki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11699)

Abstract

Advanced autonomy features of vehicles are typically difficult or impossible to specify precisely and this has led to the rise of machine learning (ML) from examples as an alternative implementation approach to traditional programming. Developing software without specifications sacrifices the ability to effectively verify the software yet this is a key component of safety assurance. In this paper, we suggest that while complete specifications may not be possible, partial specifications typically are and these could be used with ML to strengthen safety assurance. We review the types of partial specifications that are applicable for these problems and discuss the places in the ML development workflow that they could be used to improve the safety of ML-based components.

Keywords

Safety Machine learning Specification 

Notes

Acknowledgements

We would like to thank Mark Costin for insightful comments that have contributed to this work.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of WaterlooWaterlooCanada

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