Buch, Englisch, 510 Seiten, Format (B × H): 155 mm x 235 mm
4th World Conference, xAI 2026, Fortaleza, Brazil, July 1–3, 2026, Proceedings, Part III
Buch, Englisch, 510 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Communications in Computer and Information Science
ISBN: 978-3-032-45472-0
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.
Weitere Infos & Material
.- Psychometric Applications for XAI and attribution techniques.
.- Are All Instances Equally Explainable? A Study on the Impact of Locality on the Explainability of Automatic Text Classification Tasks.
.- Improving Explanations: Applying the Feature Understandability Scale for Cost-Sensitive Feature Selection.
.- A Structured Guide to Selecting Feature Attribution Techniques.
.- On the Properties of Feature Attribution for Supervised Contrastive Learning.
.- CLINT: Cluster Interpretability through Feature Selection.
.- Neural-Symbolic Methods for Interpretable and explainable AI.
.- Uncovering Latent Phase Structures and Branching Logic in Locomotion Policies: A Case Study on HalfCheetah.
.- In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks.
.- Improving Smart Building Forecasts through XAI-Derived Symbolic Correction Rules.
.- GCDiff-X: Graph-Conditioned Diffusion for Explanations.
.- Argumentation, abduction, abstraction and agreement for XAI.
.- A Causal Argumentation Method for Explainability of Machine Learning Models.
.- Concisely Explaining the Doubt: Minimum-Size Abductive Explanations for Linear Models with a Reject Option.
.- Interpretable-by-Abstraction: Declarative Features for Explainable Event Data.
.- Trustworthiness in Dynamic Environments via Semantic Stability and Explainer Agreement.
.- Shapley-driven explainable AI.
.- How Stable is SHAP under Oversampling? A Study of Explanations in Imbalanced Scenarios.
.- ExplainReduce: generating global explanations from many local explanations.
.- Computing Conditional Shapley Values Using Tabular Foundation Models.
.- SHAPCA: Consistent and Interpretable Explanations for Machine Learning Models on Spectroscopy Data.
.- Additive and well-calibrated explanations using Venn-Abers and SHAP.
.- Explainable AI in legal and regulatory contexts.
.- Legal Obligations for AI Explainability: Open Questions on Technical Methods.
.- Assessing Model-Agnostic XAI Methods against EU AI Act Explainability Requirements.
.- Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users.
.- The Role of Explainable AI to Ensure ‘Transparent’ AI Systems as Required by EU Law – Moving Beyond the Right to Explanation.
.- The Bidirectional Relationship Between XAI and Regulation: Operationalizing XAI for the AI Act.
