Explainable Artificial Intelligence

Luca Longo

ISBN 10: 3031637968 / ISBN 13: 9783031637964

Published by Springer Nature Switzerland, Springer International Publishing Jul 2024, 2024

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 532 pp. Englisch.

Seller Inventory # 9783031637964

Bibliographic Details

• Title: Explainable Artificial Intelligence
• Publisher: Springer Nature Switzerland, Springer International Publishing Jul 2024
• Publication Date: 2024
• Language: English
• ISBN 10: 3031637968
• ISBN 13: 9783031637964
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.