xxAI - Beyond Explainable Artificial Intelligence

Andreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus Robert Müller, Wojciech Samek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The success of statistical machine learning from big data, especially of deep learning, has made artificial intelligence (AI) very popular. Unfortunately, especially with the most successful methods, the results are very difficult to comprehend by human experts. The application of AI in areas that impact human life (e.g., agriculture, climate, forestry, health, etc.) has therefore led to an demand for trust, which can be fostered if the methods can be interpreted and thus explained to humans. The research field of explainable artificial intelligence (XAI) provides the necessary foundations and methods. Historically, XAI has focused on the development of methods to explain the decisions and internal mechanisms of complex AI systems, with much initial research concentrating on explaining how convolutional neural networks produce image classification predictions by producing visualizations which highlight what input patterns are most influential in activating hidden units, or are most responsible for a model’s decision. In this volume, we summarize research that outlines and takes next steps towards a broader vision for explainable AI in moving beyond explaining classifiers via such methods, to include explaining other kinds of models (e.g., unsupervised and reinforcement learning models) via a diverse array of XAI techniques (e.g., question-and-answering systems, structured explanations). In addition, we also intend to move beyond simply providing model explanations to directly improving the transparency, efficiency and generalization ability of models. We hope this volume presents not only exciting research developments in explainable AI but also a guide for what next areas to focus on within this fascinating and highly relevant research field as we enter the second decade of the deep learning revolution. This volume is an outcome of the ICML 2020 workshop on “XXAI: Extending Explainable AI Beyond Deep Models and Classifiers.”

Original languageEnglish (US)
Title of host publicationxxAI - Beyond Explainable AI - International Workshop, Held in Conjunction with ICML 2020, Revised and Extended Papers
EditorsAndreas Holzinger, Randy Goebel, Ruth Fong, Taesup Moon, Klaus-Robert Müller, Wojciech Samek
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-10
Number of pages8
ISBN (Print)9783031040825
DOIs
StatePublished - 2022
EventInternational Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, xxAI 2020, held in Conjunction with ICML 2020 - Vienna, Austria
Duration: Jul 18 2020Jul 18 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13200 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, xxAI 2020, held in Conjunction with ICML 2020
Country/TerritoryAustria
CityVienna
Period7/18/207/18/20

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Artificial intelligence
  • Explainability
  • Explainable AI
  • Machine learning

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