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

感染

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

KIN

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

GLUT

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

顾客

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

colloquial

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

Allowance

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

DRILL

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

Opponent

antenna

无可非议

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