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Book Synopsis

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.

Explainable Artificial Intelligence

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    A Paperback by Riccardo Guidotti

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      Publisher: Springer
      Publication Date: 11/13/2025
      ISBN13: 9783032083265, 978-3032083265
      ISBN10: 3032083265
      Also in:
      Digital and IT general topics

      Description

      Book Synopsis

      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.

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