Skip to main content

xxAI - Beyond Explainable AI

International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers

  • Book
  • Open Access
  • © 2022

You have full access to this open access Book

Overview

  • This book is open access, which means that you have free and unlimited access
  • Explainable AI (xAI) aims to create tools and models that are predictive, interpretable, understandable for humans
  • Topic receiving huge interest in the machine learning and AI research communities
  • Contributions in this volume are from leading researchers in the field, drawn from both academia and industry

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 13200)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

Included in the following conference series:

Conference proceedings info: xxAI 2020.

Buy print copy

Softcover Book USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Table of contents (18 chapters)

  1. Editorial

  2. Current Methods and Challenges

  3. New Developments in Explainable AI

  4. An Interdisciplinary Approach to Explainable AI

Keywords

About this book

This is an open access book.

Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans.

Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed.

After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science.



Editors and Affiliations

  • University of Natural Resources and Life Sciences Vienna, Vienna, Austria

    Andreas Holzinger

  • University of Alberta, Edmonton, Canada

    Randy Goebel

  • Princeton University, Princeton, USA

    Ruth Fong

  • Seoul National University, Seoul, Korea (Republic of)

    Taesup Moon

  • Technische Universität Berlin, Berlin, Germany

    Klaus-Robert Müller

  • Fraunhofer Heinrich Hertz Institute, Berlin, Germany

    Wojciech Samek

Bibliographic Information

  • Book Title: xxAI - Beyond Explainable AI

  • Book Subtitle: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers

  • Editors: Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-031-04083-2

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s) 2022

  • Softcover ISBN: 978-3-031-04082-5Published: 17 April 2022

  • eBook ISBN: 978-3-031-04083-2Published: 16 April 2022

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: X, 397

  • Number of Illustrations: 10 b/w illustrations, 114 illustrations in colour

  • Topics: Artificial Intelligence, Machine Learning

Publish with us