Buch, Englisch, 480 Seiten, Format (B × H): 155 mm x 235 mm
4th World Conference, xAI 2026, Fortaleza, Brazil, July 1–3, 2026, Proceedings, Part II
Buch, Englisch, 480 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Communications in Computer and Information Science
ISBN: 978-3-032-45469-0
Verlag: Springer
This open access four-volume set constitutes the refereed proceedings of the Fourth World Conference on Explainable Artificial Intelligence, xAI 2026, held in Fortaleza, Brazil, during July 1–3, 2026.
The 95 revised full papers presented in these proceedings were carefully reviewed and selected from 235 submissions. The papers are organized in the following topical sections:
Volume I: Enhancing Models with XAI: Debugging, Modification, Evaluation; Human-Centred Explanation Systems & Explanation User Interfaces; Rule-Based Explainable AI; Uncertainty-Aware Explainable AI; xAI in Healthcare: Balancing Accuracy, Trust, and Ethics.
Volume II: XAI for Time Series; Trust Through Transparency: Next-Generation Explainable AI in Medicine; Explainable AI for Sequential Decision Making; Mechanistic Interpretability in Explainable AI; XAI and the Physical Sciences.
Volume III: Psychometric Applications for XAI and attribution techniques; Neural-Symbolic Methods for Interpretable and explainable AI; Argumentation, abduction, abstraction and agreement for XAI; Shapley-driven explainable AI; Explainable AI in legal and regulatory contexts.
Volume IV: Counterfactual-based explainable AI; Explainability with and for Large Language Models; Notions and metrics for explainable AI; Contrastive-based explainable AI; Novel approaches and methods for XAI.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Mathematik | Informatik EDV | Informatik Technische Informatik Netzwerk-Hardware
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
Weitere Infos & Material
.- XAI for Time Series.
.- Evaluating Simplification Algorithms for Interpretability of Time Series Classification.
.- L2GTX: From Local to Global Time Series Explanations.
.- What-If Explanations Over Time: Counterfactuals for Time Series Classification.
.- Semantic Concept-Augmented Learning and Post-Hoc Analysis for Time Series Classification.
.- TSCFEval: A Model-Agnostic Framework for Evaluating Time Series Classification Counterfactuals.
.- SAFE Machine Learning for Financial Time Series.
.- Trust Through Transparency: Next-Generation Explainable AI in Medicine.
.- Explainable Pairwise Ranking for Clinical Prioritisation of Non-Pharmacological Intervention Candidates.
.- Explainable and Fair Risk Stratification for Surgical Referral in Drug-Resistant Epilepsy.
.- RAME: Radiologist-Audited, Mechanistically Coupled Explainability for Ultrasound AI.
.- Mechanistic Dynamic Interpretability for Tabular Foundation Models in Healthcare.
.- Explainable AI for Sequential Decision Making.
.- TREX: Trajectory Explanations for Multi-Objective Reinforcement Learning.
.- Demystifying Sequential Recommendations: Counterfactual Explanations via Genetic Algorithms.
.- Agent-Based and Knowledge-Grounded Tree-of-Thought Reasoning for Expert Question Answering.
.- Explainable AI for Streaming Process Mining: Overview, Challenges and Outlook.
.- Mechanistic Interpretability in Explainable AI.
.- XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders.
.- Analyzing Latent Manifold Paths towards Geometric Transparency for Explainable 3D Point Cloud Generation.
.- Contrastive Semantic Projection: Faithful Neuron Labeling with Contrastive Examples.
.- Locating and Editing Figure-Ground Organization in Vision Transformers.
.- Stop throwing away your Decoder; extract the learnt local coordinate system using Latent-XAI.
.- XAI and the Physical Sciences.
.- Explainable embeddings with Distance Explainer.
.- Design-Oriented Explainable AI for Metal Foam: Extracting Actionable Guidelines from Extended Geometric Descriptors.
.- Evaluating Post-hoc Explanations of the Transformer-based Genome Language Model DNABERT-2.
.- FoMo-X: Modular Explainability Signals for Outlier Detection Foundation Models.
