managementranepaУправленческое консультированиеAdministrative Consulting1726-11391816-8590Russian Presidential Academy of National Economy and Public Administration. North-West Institute of Management.CLJCAKmanagementranepa-2827Research ArticleЦИФРОВЫЕ СЕРВИСЫ И ТЕХНОЛОГИИ ИСКУССТВЕННОГО ИНТЕЛЛЕКТАИИ-ориентированные государственные сервисы: таксономия ответственности и суверенный искусственный интеллектAI-Driven Public Services: A Taxonomy of Accountability and Sovereign Artificial Intelligence (AI)НосиковА. А.NosikovА. А.

Носиков Андрей Андреевич, кандидат политических наук, старший преподаватель кафедры связей с общественностью в политике и государственном управлении Института «Высшая школа журналистики и массовых коммуникаций»

Санкт-Петербург

Andrey A. Nosikov, Ph.D. in Political Sciences, Senior Lecturer of Department of Public Relations in Politics and Public Administration

Saint Petersburg

Санкт-Петербургский государственный университетInstitute «Higher School of Journalism and Mass Communications» of Saint Petersburg State University2025051120250565–76Copyright © Носиков А.А., 20252025Носиков А.А.Nosikov А.А.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.acjournal.ru/jour/article/view/2827

Настоящее исследование анализирует институциональные и технологические вызовы интеграции систем искусственного интеллекта (ИИ) в публичное администрирование и государственные сервисы, фокусируясь на классификации ролей алгоритмов в процессах принятия решений, балансе интересов в сотрудничестве с коммерческими поставщиками ИИ-решений и инфраструктуры, а также обеспечении национальной технологической автономии. Применен качественный междисциплинарный подход, сочетающий нормативноправовой анализ, тематический анализ эмпирических кейсов из практики различных стран и теоретический синтез. Данные собраны из официальных источников, рецензируемых научных публикаций и новостных источников с использованием метода снежного кома для отбора кейсов, а кодирование проводилось итеративно. В результате разработана оригинальная авторская шестиуровневая пирамидальная модель распределения ответственности в зависимости от степени автономии алгоритмов ИИ в цепочке принятия решений: от полной делегации («ИИ-Капитан») через предложение готового решения с утверждением человеком («ИИ-Штурман»), набор конфигураций («ИИ-Советник»), анализ среды с сигнализацией триггеров («ИИ-Наблюдатель» ), выполнение трудозатратных задач с ревизией оператором («ИИ-Рабочие руки») до рутинной поддержки без решений («ИИ-Рутинный помощник»). Модель наложена на градации рисков (высокий, ограниченный, минимальный) для оценки последствий ошибок. Выявлена дилемма государственно-частного партнерства, обеспечивающего доступ к инновациям, но усиливающего зависимость и уязвимости. Также обоснована роль суверенного ИИ как стратегии снижения этих рисков. Для эффективной интеграции алгоритмов в государственные сервисы рекомендуется внедрение обязательной классификации систем ИИ по уровням автономии и критичности, где шестиуровневая таксономия обеспечивает дифференцированный подход к распределению ответственности, минимизируя институциональные пробелы и риски предвзятости. 

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. 

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The authors declare that there are no conflicts of interest present.