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Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11839))

Abstract

Deep learning has made significant contribution to the recent progress in artificial intelligence. In comparison to traditional machine learning methods such as decision trees and support vector machines, deep learning methods have achieved substantial improvement in various prediction tasks. However, deep neural networks (DNNs) are comparably weak in explaining their inference processes and final results, and they are typically treated as a black-box by both developers and users. Some people even consider DNNs (deep neural networks) in the current stage rather as alchemy, than as real science. In many real-world applications such as business decision, process optimization, medical diagnosis and investment recommendation, explainability and transparency of our AI systems become particularly essential for their users, for the people who are affected by AI decisions, and furthermore, for the researchers and developers who create the AI solutions. In recent years, the explainability and explainable AI have received increasing attention by both research community and industry. This paper first introduces the history of Explainable AI, starting from expert systems and traditional machine learning approaches to the latest progress in the context of modern deep learning, and then describes the major research areas and the state-of-art approaches in recent years. The paper ends with a discussion on the challenges and future directions.

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Author information

Authors and Affiliations

  1. AI Lab, Lenovo Research, Lenovo Group, Beijing, China

    Feiyu Xu, Yangzhou Du & Wei Fan

  2. DFKI GmbH, Germany and Giance Technologies, Saarbrucken, Germany

    Hans Uszkoreit

  3. Institute of Computer Science and Technology, Peking University, Beijing, China

    Dongyan Zhao

  4. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Jun Zhu

Authors
  1. Feiyu Xu
  2. Hans Uszkoreit
  3. Yangzhou Du
  4. Wei Fan
  5. Dongyan Zhao
  6. Jun Zhu

Corresponding author

Correspondence to Feiyu Xu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Tang

  2. National University of Singapore, Singapore, Singapore

    Min-Yen Kan

  3. Peking University, Beijing, China

    Dongyan Zhao

  4. Peking University, Beijing, China

    Sujian Li

  5. Zhengzhou University, Zhengzhou, China

    Hongying Zan

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Cite this paper

Xu, F., Uszkoreit, H., Du, Y., Fan, W., Zhao, D., Zhu, J. (2019). Explainable AI: A Brief Survey on History, Research Areas, Approaches and Challenges. In: Tang, J., Kan, MY., Zhao, D., Li, S., Zan, H. (eds) Natural Language Processing and Chinese Computing. NLPCC 2019. Lecture Notes in Computer Science(), vol 11839. Springer, Cham. https://doi.org/10.1007/978-3-030-32236-6_51

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  • DOI: https://doi.org/10.1007/978-3-030-32236-6_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32235-9

  • Online ISBN: 978-3-030-32236-6

  • eBook Packages: Computer Science Computer Science (R0)

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