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ORCID6th EXTRAAMAS 2024: Auckland, New Zealand
- Davide Calvaresi
, Amro Najjar, Andrea Omicini, Reyhan Aydogan, Rachele Carli, Giovanni Ciatto, Joris Hulstijn, Kary Främling:
Explainable and Transparent AI and Multi-Agent Systems - 6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, May 6-10, 2024, Revised Selected Papers. Lecture Notes in Computer Science 14847, Springer 2024, ISBN 978-3-031-70073-6
User-centric XAI
- Selim Karaoglu, Marina Katoh, Titash Majumdar, Ethan Beaird, Feyza Merve Hafizoglu, Sandip Sen:
Effect of Agent Explanations Using Warm and Cold Language on User Adoption of Recommendations for Bandit Problems. 3-20 - Berk Buzcu, Emre Kuru, Davide Calvaresi, Reyhan Aydogan:
Evaluation of the User-Centric Explanation Strategies for Interactive Recommenders. 21-38 - Thiago Freitas dos Santos, Nardine Osman, Marco Schorlemmer:
Can Interpretability Layouts Influence Human Perception of Offensive Sentences? 39-57 - Berk Buzcu, Yvan Pannatier, Reyhan Aydogan, Michael Ignaz Schumacher, Jean-Paul Calbimonte, Davide Calvaresi:
A Framework for Explainable Multi-purpose Virtual Assistants: A Nutrition-Focused Case Study. 58-78
XAI and Reinforcement Learning
- Mattijs Baert, Sam Leroux, Pieter Simoens:
Learning Temporal Task Specifications From Demonstrations. 81-98 - Mark Towers, Yali Du, Christopher T. Freeman, Timothy J. Norman:
Temporal Explanations of Deep Reinforcement Learning Agents. 99-115 - Sumanta Dey, Praveen Verma, Pallab Dasgupta, Soumyajit Dey:
An Adaptive Interpretable Safe-RL Approach for Addressing Smart Grid Supply-Side Uncertainties. 116-136 - Bryan Lavender, Sandip Sen:
Model-Agnostic Policy Explanations: Biased Sampling for Surrogate Models. 137-151
Neuro-symbolic AI and Explainable Machine Learning
- Victor Contreras, Michael Schumacher, Davide Calvaresi:
Explanation of Deep Learning Models via Logic Rules Enhanced by Embeddings Analysis, and Probabilistic Models. 155-183 - Kary Främling, Ioan-Vlad Apopei, Gustav Grund Pihlgren, Avleen Malhi:
py_ciu_image: A Python Library for Explaining Image Classification with Contextual Importance and Utility. 184-188 - Richard Albrecht, Joris Hulstijn, Igor Tchappi, Amro Najjar:
Towards Interactive and Social Explainable Artificial Intelligence for Digital History. 189-202
XAI and Ethics
- Timo Speith, Jing Xu:
Explainability and Transparency in Practice: A Comparison Between Corporate and National AI Ethics Guidelines in Germany and China. 205-223 - Rachele Carli, Davide Calvaresi:
The Wildcard XAI: from a Necessity, to a Resource, to a Dangerous Decoy. 224-241
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