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Explainable Artificial Intelligence 101: Techniques, Applications and Challenges

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Advanced Intelligent Computing Technology and Applications (ICIC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14089))

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Abstract

Artificial Intelligence (AI) systems have grown commonplace in modern life, with various applications from customized suggestions to self-driving vehicles. As these systems get more complicated, the necessity for transparency in their decision-making processes becomes more critical. Explainability refers to an AI system’s ability to explain how and why it made a certain judgement or prediction. Recently, there has been a surge of interest in constructing explainable AI (XAI) systems that give insights into the decision-making processes of machine learning models. This paper discloses and elaborates upon a selection of XAI techniques, identifies current challenges and possible future directions in XAI research.

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Acknowledgements

This work is funded under the AI4Cyber project, which has received funding from the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070450.

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Authors and Affiliations

  1. Bydgoszcz University of Science and Technology, Bydgoszcz, Poland

    Wiktor Kurek, Marek Pawlicki, Rafał Kozik & Michał Choraś

  2. ITTI Sp. z o.o., Poznań, Poland

    Wiktor Kurek, Marek Pawlicki, Aleksandra Pawlicka, Rafał Kozik & Michał Choraś

  3. University of Warsaw, Warsaw, Poland

    Aleksandra Pawlicka

Authors
  1. Wiktor Kurek
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  2. Marek Pawlicki
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  3. Aleksandra Pawlicka
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  4. Rafał Kozik
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  5. Michał Choraś
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Corresponding author

Correspondence to Marek Pawlicki .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Zhengzhou University of Light Industry, Zhengzhou, China

    Baohua Jin

  4. Zhong Yuan University of Technology, Zhengzhou, China

    Boyang Qu

  5. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  6. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Kurek, W., Pawlicki, M., Pawlicka, A., Kozik, R., Choraś, M. (2023). Explainable Artificial Intelligence 101: Techniques, Applications and Challenges. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_26

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  • DOI: https://doi.org/10.1007/978-981-99-4752-2_26

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  • Online ISBN: 978-981-99-4752-2

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