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Explainable Artificial Intelligence : Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part I / edited by Luca Longo, Sebastian Lapuschkin, Christin Seifert.
Springer Nature - Springer Computer Science eBooks 2024 English International Available online
View online- Format:
- Book
- Author/Creator:
- Longo, Luca.
- Series:
- Communications in Computer and Information Science, 1865-0937 ; 2153
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Natural language processing (Computer science).
- Application software.
- Computer networks.
- Artificial Intelligence.
- Natural Language Processing (NLP).
- Computer and Information Systems Applications.
- Computer Communication Networks.
- Local Subjects:
- Artificial Intelligence.
- Natural Language Processing (NLP).
- Computer and Information Systems Applications.
- Computer Communication Networks.
- Physical Description:
- 1 online resource (508 pages)
- Edition:
- 1st ed. 2024.
- Place of Publication:
- Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
- Summary:
- 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.
- Contents:
- 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.
- Notes:
- Description based on publisher supplied metadata and other sources.
- ISBN:
- 3-031-63787-9
- OCLC:
- 1446447218
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