Explainable Artificial Intelligence

Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part I

Häftad, Engelska, 2024

Av Luca Longo, Sebastian Lapuschkin, Christin Seifert

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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.

Produktinformation

Utgivningsdatum2024-07-10
Mått155 x 235 x 28 mm
Vikt768 g
FormatHäftad
SpråkEngelska
SerieCommunications in Computer and Information Science
Antal sidor494
FörlagSpringer International Publishing AG
ISBN9783031637865

Tillhör följande kategorier

Hårdvara inom Data och it
Tillämpad datateknik inom Data och it
Artificiell intelligens inom Data och it
Systemvetenskap och AI inom Data och it

.- 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.