Abstract
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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Notes
- 1.
https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn_capsule.py, Ver. f2b261b on Oct 15, 2018.
- 2.
The Hue value, in the HSV color model, can represent human perception of a color with a single value (differently from the RGB color model).
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Matsuno, Y., Ishikawa, F., Tokumoto, S. (2019). Tackling Uncertainty in Safety Assurance for Machine Learning: Continuous Argument Engineering with Attributed Tests. In: Romanovsky, A., Troubitsyna, E., Gashi, I., Schoitsch, E., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2019. Lecture Notes in Computer Science(), vol 11699. Springer, Cham. https://doi.org/10.1007/978-3-030-26250-1_33
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DOI: https://doi.org/10.1007/978-3-030-26250-1_33
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