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Machine Learning or Expert Systems that Is the Question, Which Is to Be Used by a Public Administration

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Electronic Government and the Information Systems Perspective (EGOVIS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12394))

Abstract

The article discusses the applicability of the two key areas of Artificial Intelligence - machine learning and expert systems - in Public Administration. We classify in four categories the activities of public institutions and then assign the appropriate Artificial Intelligence tools to these activities. Finally, we provide two examples from the Hungarian state administration to support our statements: the Prime Minister’s Office Knowledge Repository Project and the Flexible Tax Control Decision Support and Data Mining System of the National Tax and Customs Office.

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Notes

  1. 1.

    Extended and modified version of Artificial Intelligence in Public Administration: Expert Systems vs. Machine Learning, New Hungarian Public Administration March 2020, Volume 13, Number 1, pp. 25–30. in Hungarian.

  2. 2.

    In the rest of the paper by expert system we will understand Expert System 4.0.

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Author information

Authors and Affiliations

  1. Multilogic Ltd., 1053 Frankel Leo u. 45, Budapest, Hungary

    Iván Futó

Authors
  1. Iván Futó
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Correspondence to Iván Futó .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Corvinus University of Budapest, Budapest, Hungary

    Andrea Kő

  2. National Research Council, Florence, Italy

    Enrico Francesconi

  3. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Gabriele Kotsis

  4. Vienna University of Technology, Vienna, Wien, Austria

    A Min Tjoa

  5. Johannes Kepler University of Linz, Linz, Oberösterreich, Austria

    Ismail Khalil

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Futó, I. (2020). Machine Learning or Expert Systems that Is the Question, Which Is to Be Used by a Public Administration. In: Kő, A., Francesconi, E., Kotsis, G., Tjoa, A., Khalil, I. (eds) Electronic Government and the Information Systems Perspective. EGOVIS 2020. Lecture Notes in Computer Science(), vol 12394. Springer, Cham. https://doi.org/10.1007/978-3-030-58957-8_15

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  • DOI: https://doi.org/10.1007/978-3-030-58957-8_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58956-1

  • Online ISBN: 978-3-030-58957-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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