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xxAI - Beyond Explainable AI : International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
other
Editor(s):
Andreas Holzinger
,
Randy Goebel
,
Ruth Fong
,
Taesup Moon
,
Klaus-Robert Müller
,
Wojciech Samek
Publication date
(Print):
2022
Publisher:
Springer International Publishing
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Westminster Papers in Communication and Culture
Author and book information
Contributors
Andreas Holzinger:
(View ORCID Profile)
Klaus-Robert Müller:
(View ORCID Profile)
Wojciech Samek:
(View ORCID Profile)
Book
ISBN (Print):
978-3-031-04082-5
ISBN (Electronic):
978-3-031-04083-2
Publication date (Print):
2022
DOI:
10.1007/978-3-031-04083-2
SO-VID:
fcb90bac-60a7-4f4d-aac4-45fdf06ceae6
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Book chapters
pp. 3
xxAI - Beyond Explainable Artificial Intelligence
pp. 13
Explainable AI Methods - A Brief Overview
pp. 39
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models
pp. 69
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
pp. 91
A Rate-Distortion Framework for Explaining Black-Box Model Decisions
pp. 117
Explaining the Predictions of Unsupervised Learning Models
pp. 139
Towards Causal Algorithmic Recourse
pp. 167
Interpreting Generative Adversarial Networks for Interactive Image Generation
pp. 177
XAI and Strategy Extraction via Reward Redistribution
pp. 207
Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis
pp. 229
Interpreting and Improving Deep-Learning Models with Reality Checks
pp. 255
Beyond the Visual Analysis of Deep Model Saliency
pp. 271
ECQ\(^{\text {x}}\): Explainability-Driven Quantization for Low-Bit and Sparse DNNs
pp. 297
A Whale’s Tail - Finding the Right Whale in an Uncertain World
pp. 315
Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science
pp. 343
Varieties of AI Explanations Under the Law. From the GDPR to the AIA, and Beyond
pp. 375
Towards Explainability for AI Fairness
pp. 387
Logic and Pragmatics in AI Explanation
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