Buch, Englisch, 555 Seiten, Format (B × H): 155 mm x 235 mm
4th World Conference, xAI 2026, Fortaleza, Brazil, July 1–3, 2026, Proceedings, Part I
Buch, Englisch, 555 Seiten, Format (B × H): 155 mm x 235 mm
Reihe: Communications in Computer and Information Science
ISBN: 978-3-032-45466-9
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 Technische Informatik Netzwerk-Hardware
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Mathematik | Informatik EDV | Informatik Informatik Natürliche Sprachen & Maschinelle Übersetzung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
.- Enhancing Models with XAI: Debugging, Modification, Evaluation.
.- Explainable Federated Learning: Detecting Drift with Feature-Level Attribution Using SAGE.
.- Sign-CAM: Leveraging Binary Weights and Activations for Efficient Interpretability in Binarized Neural Networks.
.- How a Model's Architecture and Performance Influences Explainability: A Study of Grad-CAM and CRAFT in CNN-based Image Classification.
.- Analyzing Error Sources in Global Feature Effect Estimation.
.- Interpretable Survival Analysis with MultiFIX: Multimodal Feature Engineering for Risk Stratification.
.- Human-Centred Explanation Systems & Explanation User Interfaces.
.- PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations.
.- Evaluating Interpretation Risks in Interactive What-If Scenarios and Mitigating with HypotheX.
.- X-SYS: A Reference Architecture for Interactive Explanation Systems.
.- User Arousal and Advice Taking.
.- A Human-In-The-Loop Approach for Personalized Causal Recourse.
.- Rule-Based Explainable AI.
.- Circuit Representations of Random Forests with Applications to XAI.
.- Tree Model for Forest Detection: Ante-hoc and Post-hoc XAI Approach.
.- Explaining Process Variants with Human-Interpretable Rules via Surrogate Decision Model.
.- Causal-Feature Recovery via Decision Predicate Graphs.
.- A Clustering-Based Heuristic for Explainable Random Forest Compilation.
.- Uncertainty-Aware Explainable AI.
.- Uncertainty Gating for Cost-Aware Explainable Artificial Intelligence.
.- Semiotic Ablation as an Interface-Level XAI Method for Analyzing Generative Uncertainty in Large Language Models.
.- Calibrated Explanations for Within-Spec Risk Prediction: Uncertainty-Aware Decision Support.
.- No Single Metric Tells the Whole Story: A Multi-Dimensional Evaluation Framework for Uncertainty Attributions.
.- ConformaDecompose: Explaining Uncertainty via Calibration Localization.
.- Fairness Auditing Through Conformal Testing of Explanation Distributions.
.- xAI in Healthcare: Balancing Accuracy, Trust, and Ethics.
.- The High-Frequency Bias: Why Optimized XAI Ensembles Prefer Anatomical Edges Over Pathology in Medical Imaging.
.- Evaluating XAI Metric Sensitivity to Ultrasound Noise Using a U-Net Segmentation Model.
.- Evaluation of Grad-CAM Fidelity Applied to Binary Classification of Pap Smear Cytology.
.- A Human-Centered Explainability Framework for Medical AI Systems.
.- From Pixels to Pain Scales: Robust Evaluation of Explainable AI in Neonatal Pain Detection.
