Titlebook: Explainable AI with Python; Leonida Gianfagna,Antonio Di Cecco Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive

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北極人

Myocarditis

Model-Agnostic Methods for XAI,In this chapter, we start our journey through XAI model-agnostic methods that are, as we said, potent techniques to produce explanations without relying on ML model internals that are “opaque.”

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Transfusion

統(tǒng)治人類(lèi)

https://doi.org/10.1007/978-3-030-68640-6XAI; Artificial Intelligence; Machine Learning; intrinsic interpretable models; Shapley Values; Deep Tayl

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Saline

HARP

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Bernstein-test

Adversarial Machine Learning and Explainability,d by the same NN as a gibbon with 99.3% confidence. What is happening here? The first thoughts are about some mistakes in designing or training the NN, but the point that will emerge from this chapter is that this mistake in classification is due to an adversarial attack