IDEAS home Printed from https://ideas.repec.org/p/zbw/esrepo/305190.html
   My bibliography  Save this paper

Artificial Intelligence for Official Statistics: Opportunities, Practical Uses and, Challenges

Author

Listed:
  • Popoola, Osuolale Peter

Abstract

In this era of digital transformation, artificial intelligence (AI) stands out as a revolutionary force across various sectors, including production of official statistics. Artificial intelligence is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. AI technology harbors immense potential to revolutionize official statistics, from data collection, to data analysis, from data analysis to decision-making, from decision making to effective service delivery. AI tools are advancing and are becoming more prevalence. Integrating AI into official statistics by the national statistical organizations (NSOs) can be facilitated through guidance and frameworks that provide NSOs with practical knowledge on how to identify opportunities for integrating AI, assess and minimize risks, and develop phased implementation plans linked to clear lines of ownership that enable evaluation and iteration. NSOs may explore AI to enhance informed decision making, inform policies, and optimize operations. Furthermore, Safe and effective AI integration across national statistics offices have the potential to minimize administrative burdens, reduce cost against the use of traditional methods of data collection, improve decision-making and enhance public service delivery. Thus, this paper aims at developing strategies for and adopting AI capabilities for official statistics, provides guidance and frameworks for the adoption its adoption, highlights examples of how AI could be applied for official statistics, examines the governance required for responsible implementation of AI and, discusses various challenges NSOs may face when implementing Al for official statistics. Thus, providing insights into the use of AI and, the necessary conditions for ensuring responsible AI for Official statistics in this digital era.

Suggested Citation

  • Popoola, Osuolale Peter, 2024. "Artificial Intelligence for Official Statistics: Opportunities, Practical Uses and, Challenges," EconStor Research Reports 305190, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esrepo:305190
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/305190/1/Artificial%20Intelligence%20paper.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Melanie Arntz & Terry Gregory & Ulrich Zierahn, 2016. "The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis," OECD Social, Employment and Migration Working Papers 189, OECD Publishing.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claude Paraponaris, 2017. "Plateformes numériques, conception ouverte et emploi," Post-Print halshs-01614430, HAL.
    2. Caroline Lloyd & Jonathan Payne, 2021. "Fewer jobs, better jobs? An international comparative study of robots and ‘routine’ work in the public sector," Industrial Relations Journal, Wiley Blackwell, vol. 52(2), pages 109-124, March.
    3. Lütkenhorst, Wilfried, 2018. "Creating wealth without labour? Emerging contours of a new techno-economic landscape," IDOS Discussion Papers 11/2018, German Institute of Development and Sustainability (IDOS).
    4. Arntz, Melanie & Gregory, Terry & Lehmer, Florian & Matthes, Britta & Zierahn, Ulrich, 2016. "Arbeitswelt 4.0 - Stand der Digitalisierung in Deutschland: Dienstleister haben die Nase vorn (Current state of digitalisation in Germany : Service Providers are one step ahead)," IAB-Kurzbericht 201622, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    5. Liu, Shasha & Wu, Yuhuan & Kong, Gaowen, 2024. "Politics and Robots," International Review of Financial Analysis, Elsevier, vol. 91(C).
    6. Cristiano CODAGNONE & Giovanni LIVA & Egidijus BARCEVICIUS & Gianluca MISURACA & Luka KLIMAVICIUTE & Michele BENEDETTI & Irene VANINI & Giancarlo VECCHI & Emily RYEN GLOINSON & Katherine STEWART & Sti, 2020. "Assessing the impacts of digital government transformation in the EU: Conceptual framework and empirical case studies," JRC Research Reports JRC120865, Joint Research Centre.
    7. Thomas Horvath & Peter Huber & Ulrike Huemer & Helmut Mahringer & Philipp Piribauer & Mark Sommer & Stefan Weingärtner, 2022. "Mittelfristige Beschäftigungsprognose für Österreich und die Bundesländer. Berufliche und sektorale Veränderungen 2021 bis 2028," WIFO Studies, WIFO, number 70720, January.
    8. Zilian, Laura S. & Zilian, Stella S. & Jäger, Georg, 2021. "Labour market polarisation revisited: evidence from Austrian vacancy data," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 55, pages 1-7.
    9. Dario Cords & Klaus Prettner, 2022. "Technological unemployment revisited: automation in a search and matching framework [The future of work: meeting the global challenges of demographic change and automation]," Oxford Economic Papers, Oxford University Press, vol. 74(1), pages 115-135.
    10. Sandra M. Leitner & Robert Stehrer, 2019. "The Automatisation Challenge Meets the Demographic Challenge: In Need of Higher Productivity Growth," wiiw Working Papers 171, The Vienna Institute for International Economic Studies, wiiw.
    11. John Burgess & Julia Connell, 2020. "New technology and work: Exploring the challenges," The Economic and Labour Relations Review, , vol. 31(3), pages 310-323, September.
    12. Santana, Monica & Cobo, Manuel J., 2020. "What is the future of work? A science mapping analysis," European Management Journal, Elsevier, vol. 38(6), pages 846-862.
    13. Emil Sundstrup & Annette Meng & Jeppe Z. N. Ajslev & Karen Albertsen & Flemming Pedersen & Lars L. Andersen, 2022. "New Technology and Loss of Paid Employment among Older Workers: Prospective Cohort Study," IJERPH, MDPI, vol. 19(12), pages 1-13, June.
    14. Wojciech Hardy & Roma Keister & Piotr Lewandowski, 2016. "Technology or Upskilling? Trends in the Task Composition of Jobs in Central and Eastern Europe," HKUST IEMS Working Paper Series 2016-40, HKUST Institute for Emerging Market Studies, revised Dec 2016.
    15. Colombo, Emilio & Mercorio, Fabio & Mezzanzanica, Mario, 2019. "AI meets labor market: Exploring the link between automation and skills," Information Economics and Policy, Elsevier, vol. 47(C), pages 27-37.
    16. Gries, Thomas & Naudé, Wim, 2020. "Artificial Intelligence, Income Distribution and Economic Growth," IZA Discussion Papers 13606, Institute of Labor Economics (IZA).
    17. Armanda Cetrulo & Dario Guarascio & Maria Enrica Virgillito, 2020. "Anatomy of the Italian occupational structure: concentrated power and distributed knowledge," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 29(6), pages 1345-1379.
    18. Montobbio, Fabio & Staccioli, Jacopo & Virgillito, Maria Enrica & Vivarelli, Marco, 2022. "Robots and the origin of their labour-saving impact," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    19. Christine Mayrhuber & Julia Bock-Schappelwein, 2018. "Dimensionen plattformbasierter Arbeit in Österreich und Europa. Implikationen für die soziale Sicherheit," WIFO Studies, WIFO, number 61667, January.
    20. Thomsen, Stephan L, 2018. "Die Rolle der Computerisierung und Digitalisierung für Beschäftigung und Einkommen," Hannover Economic Papers (HEP) dp-645, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

    More about this item

    Keywords

    Official Statistics; Artificial Intelligence; National Statistics Organizations; Digital Era;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:esrepo:305190. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zbwkide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.