espEconomy: strategy and practiceEconomy: strategy and practice1997-99672663-550XИнститут экономики10.51176/1997-9967-2025-2-85-103esp-1601Research ArticleИННОВАЦИИ И ЦИФРОВАЯ ЭКОНОМИКАINNOVATION AND THE DIGITAL ECONOMYМашинное обучение в государственном управлении: систематический обзор применений, трендов и вызововMachine Learning in Public Governance: A Systematic Review of Applications, Trends and Challengeshttps://orcid.org/0000-0002-9321-2285НурулыЕ.NurulyY.

PhD докторант, старший преподаватель, старший научный сотрудник

пр. аль-Фараби 71, 050040, Алматы

пр. аль-Фараби 71, 050040, Алматы

PhD candidate, Senior Lecturer, Senior Research Fellow

71 al-Farabi Ave., 050040, Almaty

yeldar.nuruly@kaznu.edu.kzhttps://orcid.org/0000-0002-9992-4005СансызбаеваГ. Н.SansyzbayevaG. N.

д.э.н., профессор

пр. аль-Фараби 71, 050040, Алматы

Doc. Sc. (Econ.), Professor

71 al-Farabi Ave., 050040, Almaty

 

halima.sansyzbaeva@kaznu.edu.kzhttps://orcid.org/0000-0003-0377-7854АширбековаЛ. Ж.AshirbekovaL. Z.

к.э.н., ассоциированный профессор

пр. аль-Фараби 71, 050040, Алматы

Сand. Sc. (Econ.), Associate Professor

71 al-Farabi Ave., 050040, Almaty

laura.ashyrbekova@kaznu.edu.kzhttps://orcid.org/0009-0006-8148-0625ТажиеваС. К.TazhiyevaS. K.

к.э.н., старший преподаватель

пр. аль-Фараби 71, 050040, Алматы

Сand. Sc. (Econ.), Senior Lecturer

71 al-Farabi Ave., 050040, Almaty

samal.tazhyeva@kaznu.edu.kzКазахский национальный университет им. аль-Фараби; Центр устойчивого развития в Центральной Азии, Казахский национальный университет имени аль-Фараби в партнерстве с Гонконгским политехническим университетомКазахстанAl-Farabi Kazakh National University; Centre for Sustainable Development in Central Asia, Al-Farabi Kazakh National University in partnership with the Hong Kong Polytechnic UniversityKazakhstanКазахский национальный университет им. аль-ФарабиКазахстанAl-Farabi Kazakh National UniversityKazakhstan20251407202520285103Copyright © Нурулы Е., Сансызбаева Г.Н., Аширбекова Л.Ж., Тажиева С.К., 20252025Нурулы Е., Сансызбаева Г.Н., Аширбекова Л.Ж., Тажиева С.К.Nuruly Y., Sansyzbayeva G.N., Ashirbekova L.Z., Tazhiyeva S.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://esp.ieconom.kz/jour/article/view/1601

Сегодня активное внедрение методов машинного обучения (далее – МО) в сферу государственного управления открывает новые возможности для прогнозирования, оценки воздействия и поддержки принятия решений, одновременно порождая целый ряд этических, институциональных и контекстуальных вызовов. Данное исследование представляет собой систематизированный обзор научных публикаций, посвящённых применению МО в государственном управлении, с акцентом на выявление ключевых тематических направлений, этических рисков и барьеров институциональной интеграции. Исходный массив данных включал 524 публикации, отобранные по целевым поисковым запросам в базах Scopus и Web of Science за период 2014–2024 гг. Фильтрация данных осуществлялась с использованием SQLite, тематическое картирование проведено в среде VOSviewer, а метаданные структурированы с помощью инструмента Elicit и последующего ручного кодирования. Анализ позволил выделить четыре функциональные области применения МО в государственном управлении: прозрачность и этика, распределение ресурсов и предоставление услуг, институциональное проектирование, а также техническая интеграция. Несмотря на достигнутый прогресс в технической реализации и повышении точности прогнозирования, во многих случаях наблюдается недостаточное внедрение механизмов обеспечения справедливости, прозрачности и участия граждан. Научная новизна работы заключается в междисциплинарном синтезе и разработке типологии институциональных вызовов, возникающих при интеграции систем МО в процессы государственного управления. Перспективы дальнейших исследований связаны с эмпирической валидацией решений, развитием методов этического аудита и институциональной готовностью к ответственному, устойчивому и контекстно адаптивному применению алгоритмических инструментов в системе государственного управления.

Today, the active implementation of machine learning (hereinafter – ML) methods in public administration opens up new opportunities for forecasting, impact assessment and decision support, while simultaneously generating various challenges. The present study is aimed at a systematic review of scientific publications devoted to applying ML methods in the field of public administration, with an emphasis on identifying thematic areas, ethical and institutional challenges. The initial data set included 524 publications obtained using targeted search queries in the Scopus and Web of Science databases for the period 2014-2024. Data filtering was performed using SQLite, thematic mapping was performed in the VOSviewer environment, and metadata was structured using the Elicit tool and subsequent manual encoding. The analysis results allowed us to identify four functional areas of ML application in public administration: transparency and ethics, resource allocation and service provision, institutional design, and technical integration. Despite significant progress in the models’ technical implementation and predictive accuracy, in many cases, mechanisms for equity, transparency, and citizen participation have been poorly implemented. The scientific novelty of the work lies in the interdisciplinary synthesis and development of a typology of institutional challenges that arise when implementing ML systems in public administration. The prospects for further research are related to the empirical validation of decisions, the development of ethical audit methods, and institutional training for responsible, sustainable, and contextually adaptive use of algorithmic tools in the public administration system.

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