Explainable Artificial Intelligence

Table of Contents

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   Book Overview
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   Chapter 1 Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features
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   Chapter 2 Natural Example-Based Explainability: A Survey
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   Chapter 3 Explainable Artificial Intelligence in Education: A Comprehensive Review
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   Chapter 4 Contrastive Visual Explanations for Reinforcement Learning via Counterfactual Rewards
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   Chapter 5 Compare-xAI: Toward Unifying Functional Testing Methods for Post-hoc XAI Algorithms into a Multi-dimensional Benchmark
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   Chapter 6 Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal
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   Chapter 7 A Novel Architecture for Robust Explainable AI Approaches in Critical Object Detection Scenarios Based on Bayesian Neural Networks
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   Chapter 8 Explaining Black-Boxes in Federated Learning
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    Chapter 9 PERFEX: Classifier Performance Explanations for Trustworthy AI Systems
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    Chapter 10 The Duet of Representations and How Explanations Exacerbate It
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    Chapter 11 Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media
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    Chapter 12 Human-Computer Interaction and Explainability: Intersection and Terminology
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    Chapter 13 Explaining Deep Reinforcement Learning-Based Methods for Control of Building HVAC Systems
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    Chapter 14 Handling Missing Values in Local Post-hoc Explainability
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    Chapter 15 Necessary and Sufficient Explanations of Multi-Criteria Decision Aiding Models, with and Without Interacting Criteria
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    Chapter 16 XInsight: Revealing Model Insights for GNNs with Flow-Based Explanations
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    Chapter 17 What Will Make Misinformation Spread: An XAI Perspective
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    Chapter 18 MEGAN: Multi-explanation Graph Attention Network
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    Chapter 19 Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
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    Chapter 20 Evaluating Link Prediction Explanations for Graph Neural Networks
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    Chapter 21 Propaganda Detection Robustness Through Adversarial Attacks Driven by eXplainable AI
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    Chapter 22 Explainable Automated Anomaly Recognition in Failure Analysis: is Deep Learning Doing it Correctly?
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    Chapter 23 DExT: Detector Explanation Toolkit
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    Chapter 24 Unveiling Black-Boxes: Explainable Deep Learning Models for Patent Classification
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    Chapter 25 HOLMES: HOLonym-MEronym Based Semantic Inspection for Convolutional Image Classifiers
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    Chapter 26 Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability
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    Chapter 27 Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
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    Chapter 28 Finding Spurious Correlations with Function-Semantic Contrast Analysis
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    Chapter 29 Explaining Search Result Stances to Opinionated People
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    Chapter 30 A Co-design Study for Multi-stakeholder Job Recommender System Explanations
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    Chapter 31 Explaining Socio-Demographic and Behavioral Patterns of Vaccination Against the Swine Flu (H1N1) Pandemic
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    Chapter 32 Semantic Meaningfulness: Evaluating Counterfactual Approaches for Real-World Plausibility and Feasibility