Titlebook: Explainable Artificial Intelligence; First World Conferen Luca Longo Conference proceedings 2023 The Editor(s) (if applicable) and The Auth

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Substance P in the Nervous System,inable artificial intelligence (XAI) to understand black-box machine learning models. While many real-world applications require dynamic models that constantly adapt over time and react to changes in the underlying distribution, XAI, so far, has primarily considered static learning environments, whe

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Graham W. Taylor,Howard R. Morrises. Among the various XAI techniques, Counterfactual (CF) explanations have a distinctive advantage, as they can be generated post-hoc while still preserving the complete fidelity of the underlying model. The generation of feasible and actionable CFs is a challenging task, which is typically tackled

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Neuroleptics: Clinical Use in Psychiatry,de such feature attributions has been limited. Clustering algorithms with built-in explanations are scarce. Common algorithm-agnostic approaches involve dimension reduction and subsequent visualization, which transforms the original features used to cluster the data; or training a supervised learnin

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https://doi.org/10.1007/978-1-4613-0933-8e), compared to other features. Feature importance should not be confused with the . used by most state-of-the-art post-hoc Explainable AI methods. Contrary to feature importance, feature influence is measured against a . or .. The Contextual Importance and Utility (CIU) method provides a unified de

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The Psychopharmacology of Aggression,planations (CFEs) provide a causal explanation as they introduce changes in the original image that change the classifier’s prediction. Current counterfactual generation approaches suffer from the fact that they potentially modify a too large region in the image that is not entirely causally related

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https://doi.org/10.1007/978-1-4613-4045-4ver, the inability of these methods to consider potential dependencies among variables poses a significant challenge due to the assumption of feature independence. Recent advancements have incorporated knowledge of causal dependencies, thereby enhancing the quality of the recommended recourse action

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Robert M. Post,Frederick K. Goodwinusal structure learning algorithms. GCA generates an explanatory graph from high-level human-interpretable features, revealing how these features affect each other and the black-box output. We show how these high-level features do not always have to be human-annotated, but can also be computationall

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978-3-031-44063-2The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl