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AI Solutions and Productivity of Public Services: Insights from Poland

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Research and Innovation Forum 2020 (RIIFORUM 2020)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

The strategic objective of improving the quality of public services in modern democracies is increasingly being pursued with the use of a broad range of AI tools. Three areas seem to display a potential for efficiency enhancing transformations, namely: energy, public health and transportation. The paper describes the conditions for effective implementation of AI technologies in these areas including public regulation and expected changes in citizens’ behavior. Public policy dilemmas will be identified guided by the research question how countries can leapfrog their peers and gain on efficiency by applying AI-enhanced solutions.

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Notes

  1. 1.

    D. Rodrik suggests to guide technological developments to the benefit of lower skilled employees since (a) they have so far paid high costs of adjustments to new technologies, and (b) it is possible to steer technological developments more to the benefit of traditional middle classes—[12].

  2. 2.

    As an example of definition one can give this one: AI is “The theory and development of computer systems able to perform tasks that normally require human intelligence” ([16], p. 36).

  3. 3.

    “An algorithm is a specific set of instructions for carrying out a procedure or solving a problem, usually with the requirement that the procedure terminate at some point. Specific algorithms sometimes also go by the name method, procedure, or technique.”—see: https://mathworld.wolfram.com/Algorithm.html.

  4. 4.

    Mullainathan and Spies write: “The phrase “big data” emphasises a change in the scale of data. But, there has been an equally important change in the nature of this data. Machine learning can deal with unconventional data that is too high dimensional for standard estimation methods, including image and language information that we conventionally had not enough thought of as data set we can work with, let alone include in a regression” ([17], p. 99).

  5. 5.

    For instance, procedural complexity and stricter employment regulations.

  6. 6.

    Obegital developed by start-up Fedmind—see: https://www.fedmind.com/obegital/.

  7. 7.

    See: The Society of Automotive Engineers International’s—standard J3016. For more information—https://www.sae.org/news/2019/01/sae-updates-j3016-automated-driving-graphic—accessed 5 February 2020.

  8. 8.

    The Index measures a country readiness to use AI tools in the provision of public services and in particular “public governance”, data and infrastructure and education—more see: Oxford Insights, https://www.oxfordinsights.com/ai-readiness2019—accessed on 31 March 2020.

  9. 9.

    Trust in AI tools requires that their reliability and lack of biases is assured—[39].

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Authors and Affiliations

  1. Department of European Studies and Economic Integration, Cracow University of Economics, Rakowicka 27, 31-510, Krakow, Poland

    Aleksander Surdej

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  1. Aleksander Surdej
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Correspondence to Aleksander Surdej .

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Editors and Affiliations

  1. Research & Innovation Institute (Rii), Warsaw, Poland

    Anna Visvizi

  2. Effat University, Jeddah, Saudi Arabia

    Miltiadis D. Lytras

  3. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Naif R. Aljohani

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Surdej, A. (2021). AI Solutions and Productivity of Public Services: Insights from Poland. In: Visvizi, A., Lytras, M.D., Aljohani, N.R. (eds) Research and Innovation Forum 2020. RIIFORUM 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-62066-0_28

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  • DOI: https://doi.org/10.1007/978-3-030-62066-0_28

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