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Explanatory Interactive Machine Learning

Author

Listed:
  • Nicolas Pfeuffer

    (Goethe University Frankfurt)

  • Lorenz Baum

    (Goethe University Frankfurt)

  • Wolfgang Stammer

    (Technical University of Darmstadt)

  • Benjamin M. Abdel-Karim

    (Goethe University Frankfurt)

  • Patrick Schramowski

    (Technical University of Darmstadt)

  • Andreas M. Bucher

    (Hospital of the Goethe University Frankfurt)

  • Christian Hügel

    (Hospital of the Goethe University Frankfurt)

  • Gernot Rohde

    (Hospital of the Goethe University Frankfurt)

  • Kristian Kersting

    (Technical University of Darmstadt)

  • Oliver Hinz

    (Goethe University Frankfurt)

Abstract

The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.

Suggested Citation

  • Nicolas Pfeuffer & Lorenz Baum & Wolfgang Stammer & Benjamin M. Abdel-Karim & Patrick Schramowski & Andreas M. Bucher & Christian Hügel & Gernot Rohde & Kristian Kersting & Oliver Hinz, 2023. "Explanatory Interactive Machine Learning," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(6), pages 677-701, December.
    • Handle: RePEc:spr:binfse:v:65:y:2023:i:6:d:10.1007_s12599-023-00806-x
    DOI: 10.1007/s12599-023-00806-x
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    References listed on IDEAS

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    1. Matthew T. Mullarkey & Alan R. Hevner & Pär Ågerfalk, 2019. "An elaborated action design research process model," European Journal of Information Systems, Taylor & Francis Journals, vol. 28(1), pages 6-20, January.
    2. Christof Weinhardt & Simon Kloker & Oliver Hinz & Wil M. P. Aalst, 2020. "Citizen Science in Information Systems Research," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(4), pages 273-277, August.
    3. Arun Rai, 2020. "Explainable AI: from black box to glass box," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 137-141, January.
    4. Elizabeth Gibney, 2020. "Whose coronavirus strategy worked best? Scientists hunt most effective policies," Nature, Nature, vol. 581(7806), pages 15-16, May.
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