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ORCIDxAI 2024: Valletta, Malta
- Luca Longo
, Sebastian Lapuschkin, Christin Seifert:
Explainable Artificial Intelligence - Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part III. Communications in Computer and Information Science 2155, Springer 2024, ISBN 978-3-031-63799-5
Counterfactual Explanations and Causality for eXplainable AI
- Mario Refoyo, David Luengo:
Sub-SpaCE: Subsequence-Based Sparse Counterfactual Explanations for Time Series Classification Problems. 3-17 - Carlo Abrate, Federico Siciliano, Francesco Bonchi, Fabrizio Silvestri:
Human-in-the-Loop Personalized Counterfactual Recourse. 18-38 - Dmytro Shvetsov, Joonas Ariva, Marharyta Domnich, Raul Vicente, Dmytro Fishman:
COIN: Counterfactual Inpainting for Weakly Supervised Semantic Segmentation for Medical Images. 39-59 - Marharyta Domnich, Raul Vicente:
Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence. 60-84 - Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright:
CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. 85-107 - Martina Cinquini, Riccardo Guidotti:
Causality-Aware Local Interpretable Model-Agnostic Explanations. 108-124 - Matteo Rizzo, Cristina Conati, Daesik Jang, Hui Hu:
Evaluating the Faithfulness of Causality in Saliency-Based Explanations of Deep Learning Models for Temporal Colour Constancy. 125-142 - Nils Ole Breuer, Andreas Sauter, Majid Mohammadi, Erman Acar:
CAGE: Causality-Aware Shapley Value for Global Explanations. 143-162
Fairness, Trust, Privacy, Security, Accountability and Actionability in eXplainable AI
- Raphael C. Engelhardt, Moritz Lange, Laurenz Wiskott, Wolfgang Konen:
Exploring the Reliability of SHAP Values in Reinforcement Learning. 165-184 - Francesco Giannini, Stefano Fioravanti, Pietro Barbiero, Alberto Tonda, Pietro Liò, Elena Di Lavore:
Categorical Foundation of Explainable AI: A Unifying Theory. 185-206 - Alireza Torabian, Ruth Urner:
Investigating Calibrated Classification Scores Through the Lens of Interpretability. 207-231 - Sarah Seifi, Tobias Sukianto, Maximilian Strobel, Cecilia Carbonelli, Lorenzo Servadei, Robert Wille:
XentricAI: A Gesture Sensing Calibration Approach Through Explainable and User-Centric AI. 232-246 - Niklas Koenen, Marvin N. Wright:
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution. 247-269 - Fatima Rabia Yapicioglu, Alessandra Stramiglio, Fabio Vitali:
ConformaSight: Conformal Prediction-Based Global and Model-Agnostic Explainability Framework. 270-293 - Fatima Ezzeddine, Mirna Saad, Omran Ayoub, Davide Andreoletti, Martin Gjoreski, Ihab Sbeity, Marc Langheinrich, Silvia Giordano:
Differential Privacy for Anomaly Detection: Analyzing the Trade-Off Between Privacy and Explainability. 294-318 - Swati Sachan, Vinicius Dezem, Dale S. Fickett:
Blockchain for Ethical and Transparent Generative AI Utilization by Banking and Finance Lawyers. 319-333 - Fahmida Tasnim Lisa, Sheikh Rabiul Islam, Neha Mohan Kumar:
Multi-modal Machine Learning Model for Interpretable Malware Classification. 334-349 - Samantha Visbeek, Erman Acar, Floris den Hengst:
Explainable Fraud Detection with Deep Symbolic Classification. 350-373 - Meirav Segal, Anne-Marie George, Ingrid Chieh Yu, Christos Dimitrakakis:
Better Luck Next Time: About Robust Recourse in Binary Allocation Problems. 374-394 - Tobias Leemann, Martin Pawelczyk, Bardh Prenkaj, Gjergji Kasneci:
Towards Non-adversarial Algorithmic Recourse. 395-419 - Nijat Mehdiyev, Maxim Majlatow, Peter Fettke:
Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring. 420-438 - Brigt Arve Toppe Håvardstun, Cèsar Ferri, Kristian Flikka, Jan Arne Telle:
XAI for Time Series Classification: Evaluating the Benefits of Model Inspection for End-Users. 439-453
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