Overview

This four-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2024, held in Valletta, Malta, during July 17-19, 2024.  The 95 full papers presented were carefully reviewed and selected from 204 submissions. The conference papers are organized in topical sections on: Part I - intrinsically interpretable XAI and concept-based global explainability; generative explainable AI and verifiability; notion, metrics, evaluation and benchmarking for XAI. Part II - XAI for graphs and computer vision; logic, reasoning, and rule-based explainable AI; model-agnostic and statistical methods for eXplainable AI. Part III - counterfactual explanations and causality for eXplainable AI; fairness, trust, privacy, security, accountability and actionability in eXplainable AI. Part IV - explainable AI in healthcare and computational neuroscience; explainable AI for improved human-computer interaction and software engineering for explainability; applications of explainable artificial intelligence.

Full Product Details

Author:   Luca Longo ,  Sebastian Lapuschkin ,  Christin Seifert
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   2024 ed.
Volume:   2154
ISBN:  

9783031637964

ISBN 10:   3031637968
Pages:   514
Publication Date:   10 July 2024
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

Table of Contents

.- XAI for graphs and Computer vision. .- Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems. .- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study. .- Explainable AI for Mixed Data Clustering. .- Explaining graph classifiers by unsupervised node relevance attribution. .- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention. .- Graph Edits for Counterfactual Explanations: A comparative study. .- Model guidance via explanations turns image classifiers into segmentation models. .- Understanding the Dependence of Perception Model Competency on Regions in an Image. .- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation. .- Explainable Emotion Decoding for Human and Computer Vision. .- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification. .- Logic, reasoning, and rule-based explainable AI. .- Template Decision Diagrams for Meta Control and Explainability. .- A Logic of Weighted Reasons for Explainable Inference in AI. .- On Explaining and Reasoning about Fiber Optical Link Problems. .- Construction of artificial most representative trees by minimizing tree-based distance measures. .- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles. .- Model-agnostic and statistical methods for eXplainable AI. .- Observation-specific explanations through scattered data approximation. .- CNN-based explanation ensembling for dataset, representation and explanations evaluation. .- Local List-wise Explanations of LambdaMART. .- Sparseness-Optimized Feature Importance. .- Stabilizing Estimates of Shapley Values with Control Variates. .- A Guide to Feature Importance Methods for Scientific Inference. .- Interpretable Machine Learning for TabPFN. .- Statistics and explainability: a fruitful alliance. .- How Much Can Stratification Improve the Approximation of Shapley Values?.

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