Explainable artificial intelligence Part 1
.- 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 an...
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| Weitere Verfasser: | , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
Cham
Springer
2024
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| Schriftenreihe: | Communications in computer and information science
2153 |
| Schlagworte: |
Angewandte Informatik
> Artificial intelligence
> COMPUTERS / Artificial Intelligence
> COMPUTERS / Data Processing / Speech & Audio Processing
> COMPUTERS / Enterprise Applications
> COMPUTERS / Networking / General
> Information technology: general issues
> Künstliche Intelligenz
> Network hardware
> Netzwerk-Hardware
> Konferenzschrift
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| Zusammenfassung: | .- 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 |
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| Beschreibung: | Literaturangaben |
| Beschreibung: | xvii, 494 Seiten Illustrationen, Diagramme |
| ISBN: | 9783031637865 978-3-031-63786-5 |