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

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Algorithm-Agnostic Feature Attributions for Clusteringde 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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Feature Importance versus Feature Influence and What It Signifies for Explainable AIe), 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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ABC-GAN: Spatially Constrained Counterfactual Generation for Image Classification Explanationsplanations (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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The Importance of Time in Causal Algorithmic Recoursever, 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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