Explainable Artificial Intelligence (Record no. 88534)
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| fixed length control field | 05614nam a22005895i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-63787-2 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730172745.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240710s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031637872 |
| -- | 978-3-031-63787-2 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3 |
| 245 10 - TITLE STATEMENT | |
| Title | Explainable Artificial Intelligence |
| Sub Title | Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part I / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XVII, 494 p. 143 illus., 137 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Communications in Computer and Information Science, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | -- Intrinsically interpretable XAI and concept-based global explainability. -- Seeking Interpretability and Explainability in Binary Activated Neural Networks. -- Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challenges. -- Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model. -- Revisiting FunnyBirds evaluation framework for prototypical parts networks. -- CoProNN: Concept-based Prototypical Nearest Neighbors for Explaining Vision Models. -- Unveiling the Anatomy of Adversarial Attacks: Concept-based XAI Dissection of CNNs. -- AutoCL: AutoML for Concept Learning. -- Locally Testing Model Detections for Semantic Global Concepts. -- Knowledge graphs for empirical concept retrieval. -- Global Concept Explanations for Graphs by Contrastive Learning. -- Generative explainable AI and verifiability. -- Augmenting XAI with LLMs: A Case Study in Banking Marketing Recommendation. -- Generative Inpainting for Shapley-Value-Based Anomaly Explanation. -- Challenges and Opportunities in Text Generation Explainability. -- NoNE Found: Explaining the Output of Sequence-to-Sequence Models when No Named Entity is Recognized. -- Notion, metrics, evaluation and benchmarking for XAI. -- Benchmarking Trust: A Metric for Trustworthy Machine Learning. -- Beyond the Veil of Similarity: Quantifying Semantic Continuity in Explainable AI. -- Conditional Calibrated Explanations: Finding a Path between Bias and Uncertainty. -- Meta-evaluating stability measures: MAX-Sensitivity & AVG-Senstivity. -- Xpression: A unifying metric to evaluate Explainability and Compression of AI models. -- Evaluating Neighbor Explainability for Graph Neural Networks. -- A Fresh Look at Sanity Checks for Saliency Maps. -- Explainability, Quantified: Benchmarking XAI techniques. -- BEExAI: Benchmark to Evaluate Explainable AI. -- Associative Interpretability of Hidden Semantics with Contrastiveness Operators in Face Classification tasks. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Longo, Luca. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Lapuschkin, Sebastian. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Seifert, Christin. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-63787-2 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer Nature Switzerland : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
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| -- | computer |
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| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural language processing (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Application software. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer networks . |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural Language Processing (NLP). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer and Information Systems Applications. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computer Communication Networks. |
| 700 1# - AUTHOR 2 | |
| -- | (orcid) |
| -- | 0000-0002-2718-5426 |
| 700 1# - AUTHOR 2 | |
| -- | (orcid) |
| -- | 0000-0002-0762-7258 |
| 700 1# - AUTHOR 2 | |
| -- | (orcid) |
| -- | 0000-0002-6776-3868 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1865-0937 ; |
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