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

Ð . Ð . Nosikov

Administrative Consulting, 2025, issue 5

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

Date: 2025
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