caerCentral Asian Economic ReviewCentral Asian Economic Review2789-43982789-4401Университет Нархоз10.52821/2789-4401-2026-2-17-34caer-1766Research ArticleГЛОБАЛИЗАЦИЯ И ЦЕНТРАЛЬНАЯ АЗИЯGLOBALIZATION AND CENTRAL ASIAИнституциональное и алгоритмическое доверие в условиях цифровизации: опыт стран Центральной АзииAlgorithms of trust in the political and economic development of Central Asiahttps://orcid.org/0000-0003-3878-072XМендыбаевБ.MendybayevB.

Коргалжын

Korgalzhyn

bolashakresearchlab@gmail.comhttps://orcid.org/0000-0001-8465-0171БурбаеваП.BurbayevaP.

кандидат социологических наук, старший преподаватель, 

г. Астана

Astana

sociobp@mail.ruАйнаеваА.AinayevaA.

магистр экономических наук,

г.Астана

Astana

a.adina@mail.ruКоргалжынская экологическая обсерваторияКазахстанKorgalzhyn Ecological ObservatoryKazakhstanЕНУ им.Л.Н.ГумилеваКазахстанENU named after L.GumilevKazakhstanКазахстанский дорожный научно-исследовательский институтКазахстанKazakh Road InstituteKazakhstan202624062026021734Copyright © Мендыбаев Б., Бурбаева П., Айнаева А., 20262026Мендыбаев Б., Бурбаева П., Айнаева А.Mendybayev B., Burbayeva P., Ainayeva A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://caer.narxoz.kz/jour/article/view/1766Цель исследования

Цель исследования. Проанализировать взаимосвязь между уровнем цифровизации, внедрением технологий искусственного интеллекта и структурами институционального доверия в странах Центральной Азии, а также проанализировать их связь с восприятием цифровых технологий и структурами общественного доверия.

Методология

Методология. Методология включает сравнительный анализ международных индексов цифрового развития (E-Government Development Index, Network Readiness Index, Freedom on the Net), данных глобальных исследований доверия к искусственному интеллекту, национальной статистики по интернет-проникновению и использованию электронных государственных услуг, а также результаты сравнительного опроса респондентов в Казахстане, Кыргызстане и Узбекистане. Для интерпретации результатов использованы композитные индексы и методы сравнительного описательного анализа.

Научная новизна и ценность. Центральная Азия рассматривается как регион с асимметричным цифровым развитием: Казахстан демонстрирует высокие показатели цифровой трансформации (включая вхождение в группу стран с очень высоким уровнем развития электронного правительства), тогда как Узбекистан и Кыргызстан находятся на этапах догоняющей модернизации. В статье предложена аналитическая модель «гибридного доверия», позволяющая разграничить институциональный, социальный и алгоритмический уровни доверия. Также введено понятие «алгоритмической предвзятости в восприятии», отражающее различие между объективными характеристиками цифровых систем и субъективной интерпретацией их работы населением. Это позволяет связать макроуровневые показатели цифровизации с микроуровневыми эффектами восприятия и политического поведения.

Результаты

Результаты. Результаты демонстрируют, что высокий уровень цифровой доступности и активное использование электронных сервисов не сопровождаются пропорциональным ростом доверия к алгоритмическим системам. Наблюдаемые межстрановые различия указывают на наличие «парадокса цифровой эффективности» и гибридной конфигурации доверия в условиях цифровой трансформации региона.

Research objective

Research objective. To analyse the relationship between the level of digitalisation, the adoption of artificial intelligence technologies, and the structures of institutional trust in the countries of Central Asia, as well as to analyse their relationship with perceptions of digital technologies and structures of public trust.

Methodology

Methodology. The methodology includes a comparative analysis of international indices of digital development (E-Government Development Index, Network Readiness Index, Freedom on the Net), data from global surveys on trust in artificial intelligence, national statistics on internet penetration and the use of electronic public services, as well as the results of a comparative survey of respondents in Kazakhstan, Kyrgyzstan, and Uzbekistan.

Scientific novelty and value. Central Asia is examined as a region characterised by asymmetric digital development: Kazakhstan demonstrates high levels of digital transformation, including its inclusion among countries with a very high level of e-government development, while Uzbekistan and Kyrgyzstan remain at stages of catch-up modernisation. The article proposes an analytical model of “hybrid trust” that differentiates among institutional, social, and algorithmic levels of trust. It also introduces the concept of “algorithmic perceptual bias,” reflecting the discrepancy between the objective characteristics of digital systems and the population’s subjective interpretation of their functioning. This approach makes it possible to link macro-level indicators of digitalisation to micro-level effects on perception and political behaviour.

Results

Results. The findings demonstrate that a high level of digital accessibility and active use of electronic services are not accompanied by a proportional increase in trust in algorithmic systems. The observed crosscountry differences identified in the study confirm the presence of a “digital efficienc paradox” and a hybrid configuration of trust in the context of regional digital transformation.

цифровые медиаискусственный интеллектинституциональное довериеалгоритмическая предвзятостьЦентральная Азияцифровая трансформацияdigital mediaartificial intelligencetrustalgorithmic biasCentral Asiadigital economyИсследование выполнено при финансовой поддержке Комитета науки Министерства науки и высшего образования Республики Казахстан в рамках проекта AP26104035 «Влияние новых цифровых медиа на политическое поведение казахстанцев в контексте развития искусственного интеллекта».This research was funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan under project AP26104035, “The influence of new digital media on the political behaviour of Kazakhstanis in the context of artificial intelligence development.ReferencesUnited Nations Department of Economic and Social Affairs. Kazakhstan: Country Information - E-Government Development Index. 2022.Afroogh, S., Akbari, A., Malone, E., Kargar, M., & Alambeigi, H. (2024). Trust in AI: Progress, challenges, and future directions. Humanities and Social Sciences Communications, 11(1), 1568. https://doi.org/10.1057/s41599-024-04044-8Kepios, DataReportal. Digital 2024: Kazakhstan. 2024.Onebunne, A. P. (2022). Algorithmic bias and media manipulation: A systematic review of AI’s role in shaping public perception and political discourse. World Journal of Advanced Research and Reviews, 16(3), 1239–1249. https://doi.org/10.30574/wjarr.2022.16.3.1332Kepios, DataReportal. Digital 2024: Uzbekistan. 2024.Amanbek, Y., Balgayev, I., Batyrkhanov, K., & Tan, M. (2020). Adoption of e-government in the Republic of Kazakhstan. Journal of Open Innovation: Technology, Market, and Complexity, 6(3), 46. https://doi.org/10.3390/joitmc6030046Eurostat. Digital economy and society statistics – households and individuals. 2024.Arana-Catania, M., Lier, F.-A. V., Procter, R., Tkachenko, N., He, Y., Zubiaga, A., & Liakata, M. (2021). Citizen participation and machine learning for a better democracy. Digital Government: Research and Practice, 2(3), 1–22. https://doi.org/10.1145/3452118Goldberg R. New NTIA Data Show 13 Million More Internet Users in the U.S. in 2023 than 2021. 2024.Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & Society, 35(3), 611–623. https://doi.org/10.1007/s00146-019-00931-wBiometricUpdate. Kazakhstan sees dramatic growth in digital government use. 2025.Areeb, M., et al. (2023). Filter bubbles in recommender systems: Fact or fallacy. https://arxiv.org/abs/2307.01221Euronews. Digital inclusion, not illusion: How governments are transforming the public sector. 2025.Bach, T. A., Khan, A., Hallock, H., Beltrão, G., & Sousa, S. (2024). A systematic literature review of user trust in AI-enabled systems: An HCI perspective. International Journal of Human–Computer Interaction, 40(5), 1251–1266. https://doi.org/10.1080/10447318.2022.2138826Eurostat. How do people interact with public authorities online? 2024.Bach, T. A., Kristiansen, J. K., Babic, A., & Jacovi, A. (2024). Unpacking human-AI interaction in safety critical industries: A systematic literature review. IEEE Access, 12, 106385–106414. https://doi.org/10.1109/ACCESS.2024.3437190Inform.kz. Уровень цифровой грамотности казахстанцев составил 92,8 %. 2025.Biometric Update. (2025). Kazakhstan sees dramatic growth in digital government use. https://www.biometricupdate.com/202507/kazakhstan-sees-dramatic-growth-in-digital-government-useEurostat. Skills for the digital age – 56 % of EU citizens aged 16-74 had at least basic digital skills (2023). 2023.Bojić, L., et al. (2024). AI alignment: Assessing the global impact of recommender systems. Futures, 155, 103280. https://doi.org/10.1016/j.futures.2024.103280Пресс-служба Министерства цифрового развития, инноваций и аэрокосмической промышленности Республики Казахстан. Итоги опроса на eGov Mobile: Казахстанцы хотят обучаться использованию ИИ для работы и повседневной жизни. 2025.Chen, C., & Sundar, S. S. (2024). Communicating and combating algorithmic bias: Effects of data diversity, labeler diversity, performance bias, and user feedback on AI trust. Human–Computer Interaction, 1–37. https://doi.org/10.1080/07370024.2024.2392494Gillespie N. и др. Trust, attitudes and use of artificia intelligence: A global study 2025. The University of Melbourne, 2025. С. 4974511 Bytes.Crawford, K. (2021). Atlas of AI: Power, politics, and the planetary costs of artificial intelligence. Yale University Press.Bach T. A. и др. A Systematic Literature Review of User Trust in AI-Enabled Systems: An HCI Perspective // International Journal of Human–Computer Interaction. 2024. Т. 40, № 5. С. 1251–1266.DataReportal. (2024). Digital 2024: Kyrgyzstan. https://datareportal.com/reports/digital-2024-kyrgyzstanBach T. A. и др. Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review // IEEE Access. 2024. Т. 12. С. 106385–106414.Dogruel, L., Facciorusso, D., & Stark, B. (2022). I’m still the master of the machine: Internet users’ awareness of algorithmic decision-making and their perception of its effect on their autonomy. Information, Communication & Society, 25(9), 1311–1332. https://doi.org/10.1080/1369118X.2020.1863999Kessler T. и др. Comparisons of Human-Human Trust with Other Forms of Human-Technology Trust // Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 2017. Т. 61, № 1. С. 1303–1307.Euronews. (2025). Digital inclusion, not illusion: How governments are transforming the public sector. https://www.euronews.com/next/2025/06/24/digital-inclusion-not-illusion-how-governments-aretransforming-the-public-sectorAfroogh S. и др. Trust in AI: progress, challenges, and future directions // Humanit Soc Sci Commun. 2024. Т. 11, № 1. С. 1568.Eurostat. (2023). Skills for the digital age—56% of EU citizens aged 16–74 had at least basic digital skills (2023). https://ec.europa.eu/eurostat/statistics-explained/index.php/Skills_for_the_digital_ageHenrique B. M., Santos E. Trust in artificial intelligence: Literature review and main path analysis // Computers in Human Behavior: Artificial Humans. 2024. Т. 2, № 1. С. 100043Eurostat. (2024a). Digital economy and society statistics—Households and individuals. https://ec.europa.eu/eurostat/statistics-explained/index.php/Digital_economy_and_society_statistics_-_households_and_individualsAraujo T. и др. In AI we trust? Perceptions about automated decision-making by artificial intelligence // AI & Soc. 2020. Т. 35, № 3. С. 611–623.Eurostat. (2024b). How do people interact with public authorities online? https://ec.europa.eu/eurostat/web/products-eurostat-news/w/ddn-20240924-2Kieslich K., Keller B., Starke C. Artificial intelligence ethics by design. Evaluating public perception on the importance of ethical design principles of artificial intelligence // Big Data & Society. 2022. Т. 9, № 1. С. 20539517221092956.Freedom House. (2024a). Freedom on the Net 2024: Kazakhstan. https://freedomhouse.org/country/kazakhstan/freedom-net/2024Starke C. и др. Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literature. arXiv, 2021.Freedom House. (2024b). Freedom on the Net 2024: Tightening the net. https://freedomhouse.org/report/freedom-net/2024/tightening-netOECD. OECD Digital Economy Outlook 2024 (Volume 1). Organisation for Economic Co-operation and Development, 2024.Gandini, A., et al. (2022). The algorithmic public opinion: A literature review. SocArXiv. https://doi.org/10.31235/osf.io/m6zn8Gandini A. и др. The algorithmic public opinion: a literature review. SocArXiv, 2022.Gaurav, A., Arya, V., & Chui, K. T. (2023). A novel approach for social media content filtering using machine learning technique. In N. Nedjah, G. Martínez Pérez, & B. B. Gupta (Eds.), International Conference on Cyber Security, Privacy and Networking (ICSPN 2022) (Vol. 599, pp. 269–275). Springer. https://doi.org/10.1007/978-3-031-22018-0_25Dogruel L., Facciorusso D., Stark B. ‘I’m still the master of the machine.’ Internet users’ awareness of algorithmic decision-making and their perception of its effect on their autonomy // Information, Communication & Society. 2022. Т. 25, № 9. С. 1311–1332.Gillespie, N., Lockey, S., Ward, T., Macdade, A., & Hassed, G. (2025). Trust, attitudes and use of artificial intelligence: A global study 2025. The University of Melbourne. https://doi.org/10.26188/2882291Amaka Peace Onebunne. Algorithmic bias and media manipulation: A systematic review of AI’s Role in shaping public perception and political discourse // World J. Adv. Res. Rev. 2022. Т. 16, № 3. С. 1239–1249.Goldberg, R. (2024, June). New NTIA data show 13 million more internet users in the U.S. in 2023 than 2021. https://www.ntia.gov/blog/2024/new-ntia-data-show-13-million-more-internet-users-us-2023-2021Ludwig K. и др. Putting ‘filterbubble’ effectsto the test: evidence on the polarizing impact of ideologybased news recommendation from two experiments in Germany and the U.S. // Information, Communication & Society. 2025. С. 1–20.Goldstein, J. A., Chao, J., Grossman, S., Stamos, A., & Tomz, M. (2024). How persuasive is AIgenerated propaganda? PNAS Nexus, 3(2), pgae034. https://doi.org/10.1093/pnasnexus/pgae034Areeb M., others. Filter Bubbles in Recommender Systems: Fact or Fallacy. 2023.Henrique, B. M., & Santos, E. (2024). Trust in artificial intelligence: Literature review and main path analysis. Computers in Human Behavior: Artificia Humans, 2(1), 100043. https://doi.org/10.1016/j.chbah.2024.100043Bojić L., others. AI alignment: Assessing the global impact of recommender systems // Futures. 2024. Т. 155. С. 103280.Inform.kz. (2025). Uroven' tsifrovoy gramotnosti kazakhstantsev sostavil 92,8%. https://www.inform. kz/ru/uroven-tsifrovoy-gramotnosti-kazahstantsev-sostavil-928-6046f3Rodilosso E. Filter Bubbles and the Unfeeling: How AI for Social Media Can Foster Extremism and Polarization // Philos. Technol. 2024. Т. 37, № 2. С. 71.Kepios & DataReportal. (2024a). Digital 2024: Kazakhstan. https://datareportal.com/reports/digital2024-kazakhstanGaurav A., Arya V., Chui K. T. A Novel Approach for Social Media Content Filtering Using Machine Learning Technique // International Conference on Cyber Security, Privacy and Networking (ICSPN 2022) / под ред. Nedjah N., Martínez Pérez G., Gupta B. B. Cham: Springer International Publishing, 2023. Т. 599. С. 269–275.Kepios & DataReportal. (2024b). Digital 2024: Uzbekistan. https://datareportal.com/reports/digital2024-uzbekistanGoldstein J. A. и др. How persuasive is AI-generated propaganda? // PNAS Nexus / под ред. Contractor N. 2024. Т. 3, № 2. С. pgae034.Kessler, T., Stowers, K., Brill, J. C., & Hancock, P. A. (2017). Comparisons of human-human trust with other forms of human-technology trust. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 61(1), 1303–1307. https://doi.org/10.1177/1541931213601808Spitale G., Biller-Andorno N., Germani F. AI model GPT-3 (dis) informs us better than humans. Sci Adv 9, eadh1850, 2023.Kieslich, K., Keller, B., & Starke, C. (2022). Artificia intelligence ethics by design: Evaluating public perception on the importance of ethical design principles of artificial intelligence. Big Data & Society, 9(1). https://doi.org/10.1177/20539517221092956Savveli I., Rigou M., Balaskas S. From E-Government to AI E-Government: A Systematic Review of Citizen Attitudes // Informatics. 2025. Т. 12, № 3. С. 98.Kurmangali, M., Yeraliyeva, Y., & Beimisheva, A. (2024). Digitalization and artificial intelligence in Central Asia: Governmental responses and further implications. Public Policy and Administration, 23. https://doi.org/10.13165/VPA-24-23-2-03OECD. AI language models: Technological, socio-economic and policy considerations: OECD Digital Economy Papers 352. 2023. Т. 352.Liu, B. (2021). In AI we trust? Effects of agency locus and transparency on uncertainty reduction in human-AI interaction. Journal of Computer-Mediated Communication, 26(6), 384–402. https://doi.org/10.1093/jcmc/zmab013OECD. Governing with ArtificialIntelligence: The State of Play and Way Forward in Core Government Functions. OECD Publishing, 2025.Ludwig, K., Müller, P., Nikolajevic, N., & Grote, A. (2025). Putting filter bubble effects to the test: Evidence on the polarizing impact of ideology-based news recommendation. Information, Communication & Society, 1–20. https://doi.org/10.1080/1369118X.2024.2435998Arana-Catania M. и др. Citizen Participation and Machine Learning for a Better Democracy // Digit. Gov.: Res. Pract. 2021. Т. 2, № 3. С. 1–22.Maslej, N., et al. (2025). The AI Index 2025 annual report. Stanford University.OECD. The Protection and Promotion of Civic Space: Strengthening Alignment with International Standards and Guidance. OECD, 2022.Nations, U. (2024). United Nations E-Government Survey: Data center. https://publicadministration.un.org/egovkb/en-us/Data-CenterOECD. OECD Guidelines for Citizen Participation Processes. OECD, 2022.OECD. (2022a). OECD guidelines for citizen participation processes. https://doi.org/10.1787/f765caf6-enKurmangali M., Yeraliyeva Y., Beimisheva A. Digitalization and artificial inteligence in Central Asia: governmental responses and further implications // Public Policy and Administration. 2024. Т. 23.OECD. (2022b). The protection and promotion of civic space. https://doi.org/10.1787/d234e975-enXu J., Lee T., Goggin G. AI governance in Asia: policies, praxis and approaches // Communication Research and Practice. 2024. Т. 10, № 3. С. 275–287.OECD. (2023). AI language models: Technological, socio-economic and policy considerations (OECD Digital Economy Papers No. 352). https://doi.org/10.1787/13d38f92-enAmanbek Y. и др. Adoption of e-Government in the Republic of Kazakhstan // Journal of Open Innovation: Technology, Market, and Complexity. 2020. Т. 6, № 3. С. 46.OECD. (2024). OECD digital economy outlook 2024 (Vol. 1).Правительство РК. Об утверждении Концепции развития искусственного интеллекта на 2024– 2029 годы. 2024.OECD. (2025). Governing with artificial intelligence. https://doi.org/10.1787/795de142-ePortulans Institute. Network Readiness Index: Kazakhstan. 2023.Omirgazy, D. (2025). Kazakhstan’s digital revolution: From e-government to AI superpower. Astana Times.Freedom House. Freedom on the Net 2024: Kazakhstan. 2024.Portulans Institute. (2023). Network readiness index: Kazakhstan.Crawford K. Atlas of AI: power, politics, and the planetary costs of artificial intelligence. New Haven: Yale University Press, 2021. 1 с.Rodilosso, E. (2024). Filter bubbles and the unfeeling. Philosophy & Technology, 37(2), 71. https://doi.org/10.1007/s13347-024-00758-4Freedom House. Freedom on the Net 2024: Tightening the Net. 2024.Savveli, I., Rigou, M., & Balaskas, S. (2025). From e-government to AI e-government. Informatics, 12(3), 98. https://doi.org/10.3390/informatics12030098United Nations Department of Economic and Social Affairs (UN DESA). United Nations EGovernment Survey 2022: The Future of Digital Government. 2022.Spitale, G., Biller-Andorno, N., & Germani, F. (2023). AI model GPT-3 (dis)informs us better than humans. Science Advances, 9, eadh1850.Nations U. United Nations E-Government Survey: Data Center. 2024.Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2021). Fairness perceptions of algorithmic decision-making. arXiv. https://doi.org/10.48550/ARXIV.2103.12016DataReportal. Digital 2024: Kyrgyzstan. 2024.Stinson, C. (2022). Algorithms are not neutral. AI and Ethics, 2(4), 763–770. https://doi.org/10.1007/s43681-022-00136-wChen C., Sundar S. S. Communicating and combating algorithmic bias: effects of data diversity, labeler diversity, performance bias, and user feedback on AI trust // Human–Computer Interaction. 2024. С. 1–37.Sundar, S. S., Jia, H., Waddell, T. F., & Huang, Y. (2015). Toward a theory of interactive media effect (TIME). In S. S. Sundar (Ed.), The handbook of the psychology of communication technology (pp. 47–86). Wiley. https://doi.org/10.1002/9781118426456.ch3Liu B. In AI We Trust? Effects of Agency Locus and Transparency on Uncertainty Reduction in Human–AI Interaction // Journal of Computer-Mediated Communication. 2021. Т. 26, № 6. С. 384–402.United Nations Department of Economic and Social Affairs.(2022). Kazakhstan: Country information— E-government development index.Sundar S. S. и др. Toward a Theory of Interactive Media Effects (TIME): Four Models for Explaining How Interface Features Affect User Psychology // The Handbook of the Psychology of Communication Technology. 1-е изд. / под ред. Sundar S. S. Wiley, 2015. С. 47–86.United Nations Department of Economic and Social Affairs. (2022). United Nations e-government survey 2022.Stinson C. Algorithms are not neutral: Bias in collaborative filtering // AI Ethics. 2022. Т. 2, № 4. С. 763–770.Xu, J., Lee, T., & Goggin, G. (2024). AI governance in Asia: Policies, praxis and approaches. Communication Research and Practice, 10(3), 275–287. https://doi.org/10.1080/22041451.2024.2391204Maslej N. и др. The AI Index 2025 Annual Report. Stanford, CA: AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, 2025.Pravitel'stvo RK. (2024, July). Ob utverzhdenii Kontseptsii razvitiya iskusstvennogo intellekta na 2024–2029 gody. https://adilet.zan.kz/rus/docs/P2400000592Omirgazy D. Kazakhstan’s Digital Revolution: From e-Government to AI Superpower // Astana Times. 2025.Press-sluzhba Ministerstva tsifrovogo razvitiya, innovatsiy i aerokosmicheskoy promyshlennosti Respubliki Kazakhstan. (2025, March). Itogi oprosa na eGov Mobile: Kazakhstantsy khotyat obuchat'sya ispol'zovaniyu II dlya raboty i povsednevnoy zhizni. https://www.gov.kz/memleket/entities/mdai/press/news/ details/956034?lang=ru

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