IDEAS home Printed from https://ideas.repec.org/a/acf/journl/y2025id2827.html

AI-Driven Public Services: A Taxonomy of Accountability and Sovereign Artificial Intelligence (AI)

Author

Listed:
  • Ð . Ð . Nosikov

Abstract

This study examines the institutional and technological challenges of integrating artificial intelligence (AI) systems into public administration and governmental services, focusing on the taxonomy of algorithmic roles in decision-making, the balance of interests in cooperation with commercial AI providers and infrastructure actors, and the safeguarding of national technological sovereignty. A qualitative interdisciplinary approach is applied, combining regulatory and legal analysis, thematic examination of empirical cases across different countries, and theoretical synthesis. Data were collected from official documents, peer-reviewed publications, and news sources, using snowball sampling for case selection and iterative coding for analytical categorization. The research develops a six-tier pyramidal model of accountability distribution according to the degree of algorithmic autonomy in decision-making chains: from full delegation («AI as Captain»), provision of ready-made solutions for human approval («AI as Navigator»), configuration of option sets («AI as Adviser»), environmental analysis with trigger signaling («AI as Observer»), execution of labor-intensive tasks under operator supervision («AI as Workforce»), to routine operational support without decision-making capacity («AI as Routine Assistant»). The model is mapped against risk gradations (high, limited, minimal) to assess error consequences.The findings reveal the dilemma of public-private partnerships, which facilitate access to innovation but simultaneously reinforce dependence and systemic vulnerabilities. The study also substantiates the role of sovereign AI as a strategic response to these risks. For effective integration of AI into governmental services, it recommends mandatory classification of systems by autonomy and criticality levels. The proposed six-level taxonomy enables a differentiated approach to accountability allocation, reducing institutional gaps and risks of bias, while enhancing resilience and strategic security.Â

Suggested Citation

  • Ð . Ð . Nosikov, 2025. "AI-Driven Public Services: A Taxonomy of Accountability and Sovereign Artificial Intelligence (AI)," 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:2827
    as

    Download full text from publisher

    File URL: https://www.acjournal.ru/jour/article/viewFile/2827/2066
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anca Florentina Vatamanu & Mihaela Tofan, 2025. "Integrating Artificial Intelligence into Public Administration: Challenges and Vulnerabilities," Administrative Sciences, MDPI, vol. 15(4), pages 1-23, April.
    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. Saša Zdravković & Filip Dobrić & Zoran Injac & Violeta Lukić-Vujadinović & Milinko Veličković & Branka Bursać Vranješ & Srđan Marinković, 2025. "AI-Driven Safety Evaluation in Public Transport: A Case Study from Belgrade’s Closed Transit Systems," Sustainability, MDPI, vol. 17(18), pages 1-36, September.
    2. Domenico Trezza & Giuseppe Luca De Luca Picione & Carmine Sergianni, 2025. "AI Response Quality in Public Services: Temperature Settings and Contextual Factors," Societies, MDPI, vol. 15(5), pages 1-17, May.
    3. Loredana Maria Clim (Moga) & Mariana Man & Ionica Oncioiu, 2025. "Mapping Territorial Disparities in Artificial Intelligence Adoption Across Local Public Administrations: Multilevel Evidence from Germany," Administrative Sciences, MDPI, vol. 15(7), pages 1-21, July.
    4. Wiston Mbhazima Baloyi, 2025. "A Systematic Analysis of Ethical and Governance Concerns Relating to Artificial Intelligence Adoption in the South African Public Sector," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 9(9), pages 3177-3190, September.

    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:2827. 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.