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Explainable Artificial Intelligence : Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part II / edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
Springer Nature - Springer Computer Science eBooks 2024 English International Available online
View online- Format:
- Book
- Author/Creator:
- Longo, Luca.
- Series:
- Communications in Computer and Information Science, 1865-0937 ; 2154
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Natural language processing (Computer science).
- Application software.
- Computer networks.
- Artificial Intelligence.
- Natural Language Processing (NLP).
- Computer and Information Systems Applications.
- Computer Communication Networks.
- Local Subjects:
- Artificial Intelligence.
- Natural Language Processing (NLP).
- Computer and Information Systems Applications.
- Computer Communication Networks.
- Physical Description:
- 1 online resource (0 pages)
- Edition:
- 1st ed. 2024.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
- Summary:
- 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.
- Contents:
- 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?.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-63797-6
- OCLC:
- 1446452113
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