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Book Synopsis

Rule-based XAI Systems & Actionable Explainable AI.- CFIRE: A General Method for Combining Local Explanations.- Which LIME should I trust? Concepts, Challenges, and Solutions.- Explainable Bayesian Optimization.- Bridging the Interpretability Gap in Process Mining: A Comprehensive Approach Combining Explainable Clustering and Generative AI.- Balancing Fairness and Interpretability in Clustering with FairParTree.- Features Importance-based XAI.- Antithetic Sampling for Top-k Shapley Identification.- Detecting Concept Drift with SHapley Additive exPlanations for Intelligent Model Retraining in Energy Generation Forecasting.- Counterfactual Shapley Values for Explaining Reinforcement Learning.- Improving the Weighting Strategy in KernelSHAP.- POMELO: Black-Box Feature Attribution with Full-Input, In-Distribution Perturbations.- Novel Post-hoc & Ante-hoc XAI Approaches.- Explain to Gain: Introspective Reinforcement Learning for Enhanced Performance.- Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest.- Mathematical Foundation of Interpretable Equivariant Surrogate Models.- Interpretable Link Prediction via Neural-Symbolic Reasoning.- CausalAIME: Leveraging Peter-Clark Algorithms and Inverse Modeling for Unified Global Feature Explanation in Healthcare.- XAI for Scientific Discovery.- Interpreting the Structure of Multi-object Representations in Vision Encoders.- Leveraging Influence Functions for Resampling in PINNs.- Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features.- A Biologically Inspired Filter Significance Assessment Method for Model Explanation.

Explainable Artificial Intelligence

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    A Paperback by Riccardo Guidotti

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      Publisher: Springer
      Publication Date: 11/11/2025
      ISBN13: 9783032083234, 978-3032083234
      ISBN10: 3032083230
      Also in:
      Natural language and machine translation

      Description

      Book Synopsis

      Rule-based XAI Systems & Actionable Explainable AI.- CFIRE: A General Method for Combining Local Explanations.- Which LIME should I trust? Concepts, Challenges, and Solutions.- Explainable Bayesian Optimization.- Bridging the Interpretability Gap in Process Mining: A Comprehensive Approach Combining Explainable Clustering and Generative AI.- Balancing Fairness and Interpretability in Clustering with FairParTree.- Features Importance-based XAI.- Antithetic Sampling for Top-k Shapley Identification.- Detecting Concept Drift with SHapley Additive exPlanations for Intelligent Model Retraining in Energy Generation Forecasting.- Counterfactual Shapley Values for Explaining Reinforcement Learning.- Improving the Weighting Strategy in KernelSHAP.- POMELO: Black-Box Feature Attribution with Full-Input, In-Distribution Perturbations.- Novel Post-hoc & Ante-hoc XAI Approaches.- Explain to Gain: Introspective Reinforcement Learning for Enhanced Performance.- Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest.- Mathematical Foundation of Interpretable Equivariant Surrogate Models.- Interpretable Link Prediction via Neural-Symbolic Reasoning.- CausalAIME: Leveraging Peter-Clark Algorithms and Inverse Modeling for Unified Global Feature Explanation in Healthcare.- XAI for Scientific Discovery.- Interpreting the Structure of Multi-object Representations in Vision Encoders.- Leveraging Influence Functions for Resampling in PINNs.- Safe and Efficient Social Navigation through Explainable Safety Regions Based on Topological Features.- A Biologically Inspired Filter Significance Assessment Method for Model Explanation.

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