Explainable Artificial Intelligence
Springer International Publishing (Verlag)
978-3-031-63796-4 (ISBN)
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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.
.- 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?.
Erscheint lt. Verlag | 26.7.2024 |
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Reihe/Serie | Communications in Computer and Information Science |
Zusatzinfo | XVII, 514 p. 159 illus., 140 illus. in color. |
Verlagsort | Cham |
Sprache | englisch |
Maße | 155 x 235 mm |
Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Schlagworte | Ante-hoc approaches for interpretability • argumentative-based approaches for explanations • Artificial Intelligence • Auto-encoders & explainability of latent spaces • Case-based explanations for AI systems • causal inference & explanations • convolutional neural networks • decomposition of neural network-based models for XAI • explainability • Explainable Artificial Intelligence • Graph neural networks for explainability • Human rights for explanations in AI systems • interpretable machine learning • Interpretable representational learning • Interpreting & explaining Convolutional Neural Networks • Model-specific vs model-agnostic methods for XAI • natural language processing for explanations • Neural networks • Neuro-symbolic reasoning for XAI • reinforcement learning for enhancing XAI |
ISBN-10 | 3-031-63796-8 / 3031637968 |
ISBN-13 | 978-3-031-63796-4 / 9783031637964 |
Zustand | Neuware |
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