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Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part I /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Explainable artificial intelligence/ edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
其他題名:	second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.
其他題名:	xAI 2024
其他作者:	Longo, Luca.
團體作者:	World Conference on Explainable Artificial Intelligence
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xvii, 494 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence - Congresses. -
電子資源:	https://doi.org/10.1007/978-3-031-63787-2
ISBN:	9783031637872

Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part I /
Explainable artificial intelligencesecond World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.Part I /[electronic resource] :xAI 2024edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert. - Cham :Springer Nature Switzerland :2024. - xvii, 494 p. :ill. (some col.), digital ;24 cm. - Communications in computer and information science,21531865-0937 ;. - Communications in computer and information science ;2153..

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.

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.

ISBN: 9783031637872

Standard No.: 10.1007/978-3-031-63787-2doiSubjects--Topical Terms:

606815
Artificial intelligence
--Congresses.

LC Class. No.: Q334

Dewey Class. No.: 006.3

Explainable artificial intelligence = second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024 : proceedings.. Part I /
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