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ORCIDxxAI@ICML 2020: Vienna, Austria
- Andreas Holzinger
, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek:
xxAI - Beyond Explainable AI - International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers. Lecture Notes in Computer Science 13200, Springer 2022, ISBN 978-3-031-04082-5
Editorial
- Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek:
xxAI - Beyond Explainable Artificial Intelligence. 3-10
Current Methods and Challenges
- Andreas Holzinger, Anna Saranti, Christoph Molnar, Przemyslaw Biecek, Wojciech Samek:
Explainable AI Methods - A Brief Overview. 13-38 - Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. 39-68 - Leonard Salewski, A. Sophia Koepke, Hendrik P. A. Lensch, Zeynep Akata:
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations. 69-88
New Developments in Explainable AI
- Stefan Kolek, Duc Anh Nguyen, Ron Levie, Joan Bruna, Gitta Kutyniok:
A Rate-Distortion Framework for Explaining Black-Box Model Decisions. 91-115 - Grégoire Montavon, Jacob R. Kauffmann, Wojciech Samek, Klaus-Robert Müller:
Explaining the Predictions of Unsupervised Learning Models. 117-138 - Amir-Hossein Karimi, Julius von Kügelgen, Bernhard Schölkopf, Isabel Valera:
Towards Causal Algorithmic Recourse. 139-166 - Bolei Zhou:
Interpreting Generative Adversarial Networks for Interactive Image Generation. 167-175 - Marius-Constantin Dinu, Markus Hofmarcher, Vihang Prakash Patil, Matthias Dorfer, Patrick M. Blies, Johannes Brandstetter, Jose A. Arjona-Medina, Sepp Hochreiter:
XAI and Strategy Extraction via Reward Redistribution. 177-205 - Osbert Bastani, Jeevana Priya Inala, Armando Solar-Lezama:
Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis. 207-228 - Chandan Singh, Wooseok Ha, Bin Yu:
Interpreting and Improving Deep-Learning Models with Reality Checks. 229-254 - Sarah Adel Bargal, Andrea Zunino, Vitali Petsiuk, Jianming Zhang, Vittorio Murino, Stan Sclaroff, Kate Saenko:
Beyond the Visual Analysis of Deep Model Saliency. 255-269 - Daniel Becking, Maximilian Dreyer, Wojciech Samek, Karsten Müller, Sebastian Lapuschkin:
ECQ x: Explainability-Driven Quantization for Low-Bit and Sparse DNNs. 271-296 - Diego Marcos, Jana Kierdorf, Ted Cheeseman, Devis Tuia, Ribana Roscher:
A Whale's Tail - Finding the Right Whale in an Uncertain World. 297-313 - Antonios Mamalakis, Imme Ebert-Uphoff, Elizabeth A. Barnes:
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science. 315-339
An Interdisciplinary Approach to Explainable AI
- Philipp Hacker, Jan-Hendrik Passoth:
Varieties of AI Explanations Under the Law. From the GDPR to the AIA, and Beyond. 343-373 - Jianlong Zhou, Fang Chen, Andreas Holzinger:
Towards Explainability for AI Fairness. 375-386 - Chun-Hua Tsai, John M. Carroll:
Logic and Pragmatics in AI Explanation. 387-396
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