Explainable Artificial Intelligence

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

AvLuca Longo,Sebastian Lapuschkin

Häftad, Engelska, 2024

Del 2153 i serien Communications in Computer and Information Science

901 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2024-07-10
Mått:155 x 235 x 28 mm
Vikt:768 g
Format:Häftad
Språk:Engelska
Serie:Communications in Computer and Information Science
Antal sidor:494
Förlag:Springer International Publishing AG
ISBN:9783031637865

Utforska kategorier

Hårdvara inom Data och IT
Tillämpad datateknik inom Data och IT
Artificiell intelligens inom Data och IT

Innehållsförteckning

.- 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.