Tackling Uncertainty in Safety Assurance for Machine Learning: Continuous Argument Engineering with Attributed Tests

  • Yutaka MatsunoEmail author
  • Fuyuki Ishikawa
  • Susumu Tokumoto
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11699)


There are unique kinds of uncertainty in implementations constructed by machine learning from training data. This uncertainty affects the strategy and activities for safety assurance. In this paper, we investigate this point in terms of continuous argument engineering with a granular performance evaluation over the expected operational domain. We employ an attribute testing method for evaluating an implemented model in terms of explicit (partial) specification. We then show experimental results that demonstrate how safety arguments are affected by the uncertainty of machine learning. As an example, we show the weakness of a model, which cannot be predicted beforehand. We show our tool for continuous argument engineering to track the latest state of assurance.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.College of Science and TechnologyNihon UniversityTokyoJapan
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.Fujitsu Laboratories Ltd.KawasakiJapan

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