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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Intrinsic Explainable Models,, XAI can be achieved by looking at the internals with the proper interpretations of the weights and parameters that build the model. We will make practical examples (using Python code) that will deal with the quality of wine, the survival properties in a .-like disaster, and for the ML-addicted the

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Making Science with Machine Learning and XAI, that . .. We also provided in Table . of Chap. . (don’t worry to look at it now, we will start again from this table in the following) a set of operational criteria based on question to distinguish between interpretability as a lighter form of explainability. As we saw, explainability is able to an

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Adversarial Machine Learning and Explainability, as shown by Goodfellow et al. (2014), the first one has been classified as a panda by a NN with 55.7% confidence, while the second has been classified 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

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https://doi.org/10.1007/978-1-4614-4839-6int in this chapter about making science with ML? The answer, long story short, is that explainability is exactly what we need to climb “the ladder of causation” (we will talk about it in a while). We will use XAI in the domain of “knowledge discovery” with a specific focus on scientific knowledge.

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t is needed in the field, the book details different approaches to XAI depending on specific context and need.? Hands-on work on interpretable models with specific examples leveraging Python are then presented,978-3-030-68639-0978-3-030-68640-6

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