Overview

This open access five-volume set constitutes the refereed proceedings of the Second World Conference on Explainable Artificial Intelligence, xAI 2025, held in Istanbul, Turkey, during July 2025.  The 96 revised full papers presented in these proceedings were carefully reviewed and selected from 224 submissions. The papers are organized in the following topical sections: Volume I: Concept-based Explainable AI; human-centered Explainability; explainability, privacy, and fairness in trustworthy AI; and XAI in healthcare. Volume II: Rule-based XAI systems & actionable explainable AI; features importance-based XAI; novel post-hoc & ante-hoc XAI approaches; and XAI for scientific discovery. Volume III: Generative AI meets explainable AI; Intrinsically interpretable explainable AI; benchmarking and XAI evaluation measures; and XAI for representational alignment. Volume IV: XAI in computer vision; counterfactuals in XAI; explainable sequential decision making; and explainable AI in finance & legal frameworks for XAI technologies. Volume V: Applications of XAI; human-centered XAI & argumentation; explainable and interactive hybrid decision making; and uncertainty in explainable AI.

Full Product Details

Author:   Riccardo Guidotti ,  Ute Schmid ,  Luca Longo
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032083265

ISBN 10:   3032083265
Pages:   448
Publication Date:   12 October 2025
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Generative AI meets Explainable AI.- Reasoning-Grounded Natural Language Explanations for Language Models.- What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models.- Explainable Optimization: Leveraging Large Language Models for User-Friendly Explanations.- Large Language Models as Attribution Regularizers for Efficient Model Training.- GraphXAIN: Narratives to Explain Graph Neural Networks.- Intrinsically Interpretable Explainable AI.- MSL: Multiclass Scoring Lists for Interpretable Incremental Decision Making.- Interpretable World Model Imaginations as Deep Reinforcement Learning Explanation.- Unsupervised and Interpretable Detection of User Personalities in Online Social Networks.- An Interpretable Data-Driven Approach for Modeling Toxic Users Via Feature Extraction.- Assessing and Quantifying Perceived Trust in Interpretable Clinical Decision Support.- Benchmarking and XAI Evaluation Measures.- When can you Trust your Explanations? A Robustness Analysis on Feature Importances.- XAIEV – a Framework for the Evaluation of XAI-Algorithms for Image Classification.- From Input to Insight: Probing the Reasoning of Attention-based MIL Models.- Uncovering the Structure of Explanation Quality with Spectral Analysis.- Consolidating Explanation Stability Metrics.- XAI for Representational Alignment.- Reduction of Ocular Artefacts in EEG Signals Based on Interpretation of Variational Autoencoder Latent Space.- Syntax-Guided Metric-Based Class Activation Mapping.- Which Direction to Choose? An Analysis on the Representation Power of Self-Supervised ViTs in Downstream Tasks.- XpertAI: Uncovering Regression Model Strategies for Sub-manifolds.- An XAI-based Analysis of Shortcut Learning in Neural Networks.

Reviews

Author Information

Tab Content 6

Author Website:  

Countries Available

All regions