Explainable Artificial Intelligence Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part I /
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 a...
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| Altri autori: | , , |
| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edizione: | 1st ed. 2024. |
| Serie: | Communications in Computer and Information Science,
2153 |
| Soggetti: | |
| Accesso online: | https://doi.org/10.1007/978-3-031-63787-2 |
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Sommario:
- -- 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.