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

前奏曲

hangdog

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

JOT

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

記憶法

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

小卷發(fā)

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

Ejaculate

cornucopia

LEERY

CAMEO