TY  - BOOK
AU  - Longo,Luca
AU  - Lapuschkin,Sebastian
AU  - Seifert,Christin
ED  - SpringerLink (Online service)
TI  - Explainable Artificial Intelligence: Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part I 
T2  - Communications in Computer and Information Science,
SN  - 9783031637872
AV  - Q334-342 
U1  - 006.3 23
PY  - 2024///
CY  - Cham
PB  - Springer Nature Switzerland, Imprint: Springer
KW  - Artificial intelligence
KW  - Natural language processing (Computer science)
KW  - Application software
KW  - Computer networks 
KW  - Artificial Intelligence
KW  - Natural Language Processing (NLP)
KW  - Computer and Information Systems Applications
KW  - Computer Communication Networks
N1  -  -- 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
N2  - 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
UR  - http://libcon.rec.uabc.mx:2048/login?url=https://doi.org/10.1007/978-3-031-63787-2
ER  -