Explainable artificial intelligence Part 2
.- 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 unsuper...
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| Weitere Verfasser: | , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
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| Schriftenreihe: | Communications in computer and information science
2154 |
| Schlagworte: |
Angewandte Informatik
> Artificial intelligence
> COMPUTERS / Artificial Intelligence
> COMPUTERS / Data Processing / Speech & Audio Processing
> COMPUTERS / Enterprise Applications
> COMPUTERS / Networking / General
> Information technology: general issues
> Künstliche Intelligenz
> Natural language & machine translation
> Natürliche Sprachen und maschinelle Übersetzung
> Network hardware
> Netzwerk-Hardware
> Konferenzschrift
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| Zusammenfassung: | .- 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?. 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 |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xvii, 514 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031637964 978-3-031-63796-4 |