Description
Book SynopsisConcept-based Explainable AI.- Global Properties from Local Explanations with Concept Explanation Clusters.- From Colors to Classes: Emergence of Concepts in Vision Transformers.- V-CEM: Bridging Performance and Intervenability in Concept-based Models.- Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations.- Concept Extraction for Time Series with ECLAD-ts.- Human-Centered Explainability.- A Nexus of Explainability and Anthropomorphism in AI-Chatbots.- Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection.- Generating Rationales Based on Human Explanations for Constrained Optimization.- Algorithmic Knowability: a unified approach to Explanations in the AI Act.- Predicting Satisfaction of Counterfactual Explanations from Human Ratings of Explanatory Qualities.- Explainability, Privacy, and Fairness in Trustworthy AI.- Too Sure for Trust. The Paradoxical Effect of Calibrated Confidence in case of Uncalibrated Trust in Hybrid Decision Making.- The Impact of Concept Explanations and Interventions on Human-machine Collaboration.-Leaking LoRA: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models.- Exploring Explainability in Federated Learning: A Comparative Study on Brain Age Prediction.- The Dynamics of Trust in XAI: Assessing Perceived and Demonstrated Trust Across Interaction Modes and Risk Treatments.- XAI in Healthcare.- Systematic Benchmarking of Local and Global Explainable AI Methods for Tabular Healthcare Data.- A Combination of Integrated Gradients and SRFAMap for Explaining Neural Networks Trained with High-order Statistical Radiomic Features.- FAIR-MED: Bias Detection and Fairness Evaluation in Healthcare Focused XAI.- Weakly Supervised Pixel-Level Annotation with Visual Interpretability.- Assessing the Value of Explainable Artificial Intelligence for Magnetic Resonance Imaging.
