Titlebook: AI Verification; First International Guy Avni,Mirco Giacobbe,Christian Schilling Conference proceedings 2024 The Editor(s) (if applicable)

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978-3-031-65111-3The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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AI Verification978-3-031-65112-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Pharmakologie, Gerinnung und Mikrochirurgiethough we demonstrate bottlenecks in existing tools when handling the required specifications. We demonstrate the approach’s efficacy by applying it to a vision-based autonomous airplane taxiing system and compare with a fixed frequency analysis baseline.

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Mikrochirurgische Nervenkoaptationof instances. We observed that the robustness of the same class over the same data can significantly differ from each other for different neural networks; this means that even when a neural network appears to be unbiased, it might be easier to perturb instances of a given class so that they are misc

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https://doi.org/10.1007/978-3-8348-9054-2vel of difficulty. Experimental results show that for this dataset: (i) LLMs are reasonably successful at automatically generating formal specifications; and (ii) our consistency checker achieves a promising acceptance rate (up to .) for correct instances while maintaining zero tolerance for adversa

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,Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective,under-informative explanations. We demonstrate how these concepts can be effectively used to understand potential errors of existing SVA methods. In particular, for the widely deployed assumption-based SVAs, we find that they can easily be under-informative due to the distribution drift caused by di

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