IDEAS home Printed from https://ideas.repec.org/a/acf/journl/y2025id2824.html

Implementation of Artificial Intelligence in E-Government Services: Analysis and Prospects

Author

Listed:
  • V. Ð . Belyi
  • Ð . V. Chugunov

Abstract

This article serves as a preparatory study for a research project aimed at identifying the most likely socio-political and institutional changes associated with the implementation of artificial intelligence (AI) in electronic government services in Russia. The methodology is based on neoinstitutional and network approaches, as well as principles of rational choice theory. This allows for the analysis of formal and informal rules, coordination between actors, and the motivations behind their behavior. The source material includes publications from the Russian Science Citation Index (RSCI), Scopus, WoS, and IEEE databases, government policy documents, and data onAI implementation in various sectors. Particular attention is paid to examining the benefits, risks, and changes associated with the ongoing integration of AI into government services. The reviewed cases of new technology implementation demonstrate significant potential for reforming public administration, improving service efficiency, and improving communication between authorities and citizens. Significant risks associated with the implementation of AI in electronic government services are highlighted. The analysis demonstrates that the successful implementation of AI can be ensured by a balanced strategy that considers security, transparency, and the ability to trust technology. This article presents the interim results of a research project aimed at identifying digital behavior strategies for specific age groups. Younger and middle-aged generations fear the replacement of humans by AI tools, while older generations are unprepared for digital transformation. Based on the identified trends and scenarios for the implementation of AI tools in electronic services, a source study and methodological framework for the upcoming research project has been developed.Â

Suggested Citation

  • V. Ð . Belyi & Ð . V. Chugunov, 2025. "Implementation of Artificial Intelligence in E-Government Services: Analysis and Prospects," Administrative Consulting, Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management., issue 5.
  • Handle: RePEc:acf:journl:y:2025:id:2824
    as

    Download full text from publisher

    File URL: https://www.acjournal.ru/jour/article/viewFile/2824/2063
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abdoul‐Akim Wandaogo, 2022. "Does digitalization improve government effectiveness? Evidence from developing and developed countries," Applied Economics, Taylor & Francis Journals, vol. 54(33), pages 3840-3860, July.
    2. Kuziemski, Maciej & Misuraca, Gianluca, 2020. "AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings," Telecommunications Policy, Elsevier, vol. 44(6).
    3. Abdoul-Akim Wandaogo, 2022. "Does digitalization improve government effectiveness ? Evidence from developing and developed countries," Post-Print hal-03620113, HAL.
    4. Jon Kleinberg & Himabindu Lakkaraju & Jure Leskovec & Jens Ludwig & Sendhil Mullainathan, 2018. "Human Decisions and Machine Predictions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 237-293.
    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. Jiang, Yulai, 2025. "Government digital governance and corporate green total factor productivity," International Review of Economics & Finance, Elsevier, vol. 102(C).
    2. Okorie, David Iheke & Adedeji, Adeniran & Ifionu, Chinedu, 2025. "Assessing digitalization and the economy: A dynamic recursive CGE modelling approach," Telecommunications Policy, Elsevier, vol. 49(4).
    3. Laureti, Lucio & Costantiello, Alberto & Leogrande, Angelo, 2023. "The Role of Government Effectiveness in the Light of ESG Data at Global Level," MPRA Paper 115998, University Library of Munich, Germany.
    4. Zheng, Xian & Du, Xinyi & Wu, Weihao, 2025. "The impact of digital government on cross-regional investment: Evidence from Chinese cities," Economic Analysis and Policy, Elsevier, vol. 87(C), pages 99-122.
    5. World Bank, 2024. "Cross-Country Empirical Analysis of GovTech Platforms on Citizen Engagement," Policy Research Working Paper Series 10716, The World Bank.
    6. Tian Yingying & Gooi Leong Mow & Kim Mee Chong, 2025. "Harnessing AI and technological innovation for financial development: the mediating effect of government effectiveness in G20 economies," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-12, December.
    7. Antonietti, Roberto & Burlina, Chiara & Rodriguez-Pose, Andres, 2025. "Digital technology and regional income inequality: are better institutions the solution?," LSE Research Online Documents on Economics 127062, London School of Economics and Political Science, LSE Library.
    8. Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
    9. D’Acunto, Francesco & Ghosh, Pulak & Rossi, Alberto G., 2026. "How costly are cultural biases? Evidence from FinTech," Journal of Financial Economics, Elsevier, vol. 175(C).
    10. Richard Berk, 2019. "Accuracy and Fairness for Juvenile Justice Risk Assessments," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 16(1), pages 175-194, March.
    11. Peter Leopold S. Bergman & Elizabeth Kopko & Julio Rodriguez, 2021. "Using Predictive Analytics to Track Students: Evidence from a Seven-College Experiment," CESifo Working Paper Series 9157, CESifo.
    12. Bauer, Kevin & Gill, Andrej, 2021. "Mirror, mirror on the wall: Machine predictions and self-fulfilling prophecies," SAFE Working Paper Series 313, Leibniz Institute for Financial Research SAFE.
    13. McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
    14. Peng, Qiao & McKillop, Donal & Quinn, Barry & Liu, Kailong, 2025. "Modeling and predicting failure in US credit unions," International Journal of Forecasting, Elsevier, vol. 41(3), pages 1237-1259.
    15. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
    16. Fernanda Sobrino & Adolfo De Unánue T. & Edgar Hernández & Patricia Villa & Elena Villalobos & David Aké & Stephany Cisneros & Cristian Paul Camacho Osnay & Armando García Neri & Israel Hernández, 2026. "Designing AI for Prosecutorial Governance: Case Prioritization and Statutory Oversight in Mexico," Working Paper Series of the School of Government and Public Transformation 24, School of Governement and Public Transformation.
    17. Valerio Capraro & Roberto Di Paolo & Veronica Pizziol, 2023. "Assessing Large Language Models' ability to predict how humans balance self-interest and the interest of others," Papers 2307.12776, arXiv.org, revised Feb 2024.
    18. Elliott Ash & Claudia Marangon, 2024. "Judging disparities: Recidivism risk, image motives and in-group bias on Wisconsin criminal courts," Discussion Papers 2024-03, Nottingham Interdisciplinary Centre for Economic and Political Research (NICEP).
    19. Yoan Hermstrüwer & Pascal Langenbach, 2022. "Fair Governance with Humans and Machines," Discussion Paper Series of the Max Planck Institute for Behavioral Economics 2022_04, Max Planck Institute for Behavioral Economics, revised 01 Mar 2023.
    20. Daniela Sele & Marina Chugunova, 2023. "Putting a Human in the Loop: Increasing Uptake, but Decreasing Accuracy of Automated Decision-Making," Rationality and Competition Discussion Paper Series 438, CRC TRR 190 Rationality and Competition.

    More about this item

    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:acf:journl:y:2025:id:2824. 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: the person in charge (email available below). General contact details of provider: https://sziu.ranepa.ru .

    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.