Abstract
While the model evaluation methods in Chap. 2 can provide certain insights into the quality of machine learning models, their evaluations completely rely on the training and test datasets, without considering other factors that might potentially compromise the performance of a machine learning model. Actually, safety risks may appear at any stage of the lifecycle of a machine learning model. In recent years, the discussion on the potential risks of machine learning models has been very intense, and many risks have been discovered, even though the models have achieved excellent performance with respect to the traditional model evaluation methods. This chapter will discuss a set of safety and security properties.
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Huang, X., Jin, G., Ruan, W. (2023). Safety and Security Properties. In: Machine Learning Safety. Artificial Intelligence: Foundations, Theory, and Algorithms. Springer, Singapore. https://doi.org/10.1007/978-981-19-6814-3_3
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DOI: https://doi.org/10.1007/978-981-19-6814-3_3
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