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245 1 0 _aExplainable Artificial Intelligence
_h[electronic resource] :
_bSecond World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part I /
_cedited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXVII, 494 p. 143 illus., 137 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
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347 _atext file
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490 1 _aCommunications in Computer and Information Science,
_x1865-0937 ;
_v2153
505 0 _a -- 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 _aThis 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.
650 0 _aArtificial intelligence.
_93407
650 0 _aNatural language processing (Computer science).
_94741
650 0 _aApplication software.
_9105343
650 0 _aComputer networks .
_931572
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aNatural Language Processing (NLP).
_931587
650 2 4 _aComputer and Information Systems Applications.
_9105345
650 2 4 _aComputer Communication Networks.
_9105347
700 1 _aLongo, Luca.
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700 1 _aLapuschkin, Sebastian.
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700 1 _aSeifert, Christin.
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_9105353
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031637865
776 0 8 _iPrinted edition:
_z9783031637889
830 0 _aCommunications in Computer and Information Science,
_x1865-0937 ;
_v2153
_9105355
856 4 0 _uhttps://doi.org/10.1007/978-3-031-63787-2
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