Мемлекеттік басқарудағы машиналық оқыту: қолдану салалары, үрдістері мен сынтегеуріндерге жүйелі шолу

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.

1. Ahern, D. (2025). The New Anticipatory Governance Culture for Innovation: Regulatory Foresight, Regulatory Experimentation and Regulatory Learning. European Business Organization Law Review. https://doi.org/10.1007/s40804-025-00348-7

2. Akter, S., Dwivedi, Y. K., Sajib, S., Biswas, K., Bandara, R. J., & Michael, K. (2022). Algorithmic bias in machine learning-based marketing models. Journal of Business Research, 144, 201–216. https://doi.org/https://doi.org/10.1016/j.jbusres.2022.01.083

3. Alexopoulos, C., Diamantopoulou, V., Lachana, Z., Charalabidis, Y., Androutsopoulou, A., & Loutsaris, M. A. (2019). How machine learning is changing e-government. ACM International Conference Proceeding Series, Part F1481, 354–363. https://doi.org/10.1145/3326365.3326412

4. Aljuneidi, S., Heuten, W., Tepe, M., & Boll, S. (2023). Did that AI just Charge me a Fine? Citizens’ Perceptions of AI-based Discretion in Public Administration. ACM International Conference Proceeding Series, 57–67. https://doi.org/10.1145/3582515.3609518

5. Allen, G., & Owens, M. (2010). The Definitive Guide to SQLite (2nd ed.). Apress. https://doi.org/10.1007/9781-4302-3226-1

6. Arnstein, S. R. (2019). A Ladder of Citizen Participation. Journal of the American Planning Association, 85(1), 24–34. https://doi.org/10.1080/01944363.2018.15 59388

7. Arora, A., Gupta, M., Mehmi, S., Khanna, T., Chopra, G., Kaur, R., & Vats, P. (2024). Towards Intelligent Governance: The Role of AI in Policymaking and Decision Support for E-Governance. In Smart Innovation, Systems and Technologies (Vol. 379). https://doi.org/10.1007/978-981-99-8612-5_19

8. Ayling, J., & Chapman, A. (2021). Putting AI ethics to work: are the tools fit for purpose? AI and Ethics, 2, 405–429. https://doi.org/10.1007/s43681-021-00084-x

9. Barn, B. S. (2020). Mapping the public debate on ethical concerns: algorithms in mainstream media. Journal of Information, Communication and Ethics in Society, 18(1), 38–53. https://doi.org/10.1108/JICES-04-2019-0039

10. Bono Rossello, N., Simonofski, A., & Castiaux, A. (2025). Artificial intelligence for digital citizen participation: Design principles for a collective intelligence architecture. Government Information Quarterly, 42(2), 102020. https://doi.org/https://doi.org/10.1016/j.giq.2025.102020

11. Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133). https://doi.org/10.1098/rsta.2018.0080

12. Criado, J Ignacio, Sandoval-Almazán, Rodrigo, & Gil-Garcia, J Ramon. (2024). Artificial intelligence and public administration: Understanding actors, governance, and policy from micro, meso, and macro perspectives. Public Policy and Administration, 40(2), 173–184. https://doi.org/10.1177/09520767241272921

13. Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM. Procedia Computer Science, 179, 480–487. https://doi.org/10.1016/j.procs.2021.01.031

14. Ejjami, R. (2024). Integrative Literature Review 5.0: Leveraging Ai and Emerging Technologies to Redefine Academic Research. International Journal For Multidisciplinary Research. https://doi.org/10.36948/ijfmr.2024.v06i05.28756

15. Fadhel, M. A., Duhaim, A. M., Saihood, A., Sewify, A., Al-Hamadani, M. N. A., Albahri, A. S., Alzubaidi, L., Gupta, A., Mirjalili, S., & Gu, Y. (2024). Comprehensive systematic review of information fusion methods in smart cities and urban environments. Information Fusion, 107, 102317. https://doi.org/10.1016/J.INFFUS.2024.102317

16. Feiler, J. (2015). Using SQLite Basics: Storing and Retrieving Data. In J. Feiler (Ed.), Introducing SQLite for Mobile Developers (pp. 15–27). Apress. https://doi.org/10.1007/978-1-4842-1766-5_3

17. Floridi, L., & Cowls, J. (2022). A unified framework of five principles for AI in society. In Machine Learning and the City: Applications in Architecture and Urban Design.

18. Gamage, P. (2016). New development: Leveraging ‘big data’ analytics in the public sector. Public Money & Management, 36(5), 385–390. https://doi.org/10.1080/09540962.2016.1194087

19. Guerreiro, J., Garriga, R., Lozano Bagén, T., Sharma, B., Karnik, N. S., & Matić, A. (2024). Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises. Npj Digital Medicine, 7(1), 227. https://doi.org/10.1038/s41746-02401203-8

20. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019). XAI-Explainable artificial intelligence. Science Robotics, 4(37), eaay7120. https://doi.org/10.1126/scirobotics.aay7120

21. Henman, P. (2020). Improving public services using artificial intelligence: possibilities, pitfalls, governance. Asia Pacific Journal of Public Administration, 42(4), 209–221. https://doi.org/10.1080/23276665.2020.1816188

22. Huang, Y., Zhang, X., & Li, Y. (2023). A Novel Hybrid Model for PM2.5 Concentration Forecasting Based on Secondary Decomposition Ensemble and Weight Combination Optimization. IEEE Access, 11, 119748–119765. https://doi.org/10.1109/ACCESS.2023.3327707

23. Iftikhar, R., & Khan, Dr. M. (2020). Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework. Journal of Global Information Management, 28, 103–120. https://doi.org/10.4018/JGIM.2020010106

24. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399. https://doi.org/10.1038/s42256-019-0088-2

25. Keller, P., & Drake, A. (2021). Exclusivity and paternalism in the public governance of explainable AI. Computer Law & Security Review, 40, 105490. https://doi.org/10.1016/J.CLSR.2020.105490

26. Khan, M. S., Umer, H., & Faruqe, F. (2024). Artificial intelligence for low income countries. Humanities and Social Sciences Communications, 11(1), 1422. https://doi.org/10.1057/s41599-024-03947-w

27. Khikmat, R., Otabek, K., Shokhida, Y., & Khurmat, O. (2021). Developing a model and algorithm for decision support in self-government bodies using machine learning. International Conference on Information Science and Communications Technologies: Applications, Trends and Opportunities, ICISCT 2021. https://doi.org/10.1109/ICISCT52966.2021.9670157

28. Krijger, J. (2024). What About Justice and Power Imbalances? A Relational Approach to Ethical Risk Assessments for AI. Digit. Soc., 3, 56. https://doi.org/10.1007/s44206-024-00139-6

29. Lahdili, N., Onder, M., & Nyadera, I. (2024). Artificial Intelligence and Citizen Participation in Governance: Opportunities and Threats. Amme Idaresi Dergisi, 57, 202–229.

30. Leslie, D. (2019). Understanding artificial intelligence ethics and safety: A guide for the responsible design and implementation of AI systems in the public sector . Zenodo. https://doi.org/10.5281/zenodo.3240529

31. Long, Y., & Gil-Garcia, J. R. (2023). Understanding the Extent of Automation and Process Transparency Appropriate for Public Services: The AI Cases in Chinese Local Governments. International Journal of Electronic Government Research, 19(1). https://doi.org/10.4018/IJEGR.322550

32. Madan, R., & Ashok, M. (2023). AI adoption and diffusion in public administration: A systematic literature review and future research agenda. Government Information Quarterly, 40(1), 101774. https://doi.org/10.1016/J.GIQ.2022.101774

33. Maffei, S., Francesco, L., & and Villari, B. (2020). Data-driven anticipatory governance. Emerging scenarios in data for policy practices. Policy Design and Practice, 3(2), 123-134. https://doi.org/10.1080/25741292.2020.1763896

34. Masoud, N. (2025). Artificial intelligence and unemployment dynamics: an econometric analysis in high-income economies. Technological Sustainability, 4(1), 30–50. https://doi.org/10.1108/TECHS-04-2024-0033

35. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A Survey on Bias and Fairness in Machine Learning. ACM Comput. Surv., 54(6). https://doi.org/10.1145/3457607

36. Munné, R. (2016). Big Data in the Public Sector. Springer International Publishing, Cham. https://doi.org/10.1007/978-3-319-21569-3_11

37. Murata, T. (2022). Policy Making Based on Real-Scale Social Simulations. 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2022. https://doi.org/10.1109/SCISISIS55246.2022.10001860

38. Ogunleye, O. S. (2024). Using artificial intelligence to enhance e-government services delivery through data science and machine learning. In Machine Learning and Data Science Techniques for Effective Government Service Delivery. https://doi.org/10.4018/978-1-6684-97166.ch001

39. Osman, B. M., & Muse, A. M. S. (2024). Predictive analysis of Somalia’s economic indicators using advanced machine learning models. Cogent Economics and Finance, 12(1). https://doi.org/10.1080/23322039.2024.2426535

40. Otley, A., Morris, M., Newing, A., & Birkin, M. (2021). Local and application-specific geodemographics for data-led urban decision making. Sustainability (Switzerland), 13(9), 4873. https://doi.org/10.3390/su13094873

41. Papadakis, T., Christou, I. T., Ipektsidis, C., Soldatos, J., & Amicone, A. (2024). Explainable and transparent artificial intelligence for public policymaking. Data & Policy, 6, e10. https://doi.org/10.1017/dap.2024.3

42. Qiu, J., & Zhao, Y. (2025). Traffic Prediction with Data Fusion and Machine Learning. Analytics, 4(2). https://doi.org/10.3390/analytics4020012

43. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44. https://doi.org/10.1145/3351095.3372873

44. Rezk, M. A., Ojo, A., El Khayat, G. A., & Hussein, S. (2018). A Predictive Government Decision Based on Citizen Opinions: Tools & Results. Proceedings of the 11th International Conference on Theory and Practice of Electronic Governance, 712–714. https://doi.org/10.1145/3209415.3209504

45. Ridley, M. (2022). Explainable Artificial Intelligence (XAI): Adoption and Advocacy. Information Technology and Libraries, 41(2). https://doi.org/10.6017/ital.v41i2.14683

46. Sanchez, T. W., Brenman, M., & Ye, X. (2025). The Ethical Concerns of Artificial Intelligence in Urban Planning. Journal of the American Planning Association, 91(2), 294–307. https://doi.org/10.1080/01944363.2024.2355305

47. Satri, J., El Mokhi, C., & Hachimi, H. (2024). Predicting the outcome of regional development projects using machine learning. IAES International Journal of Artificial Intelligence, 13(1), 863–875. https://doi.org/10.11591/ijai.v13.i1.pp863-875

48. Sharma, M., Luthra, S., Joshi, S., & Kumar, A. (2022). Implementing challenges of artificial intelligence: Evidence from public manufacturing sector of an emerging economy. Government Information Quarterly, 39(4), 101624. https://doi.org/https://doi.org/10.1016/j.giq.2021.101624

49. Spillias, S., Tuohy, P., Andreotta, M., Annand-Jones, R., Boschetti, F., Cvitanovic, C., Duggan, J., Fulton, E., Karcher, D., Paris, C., Shellock, R., & Trebilco, R. (2024). Human-AI collaboration to identify literature for evidence synthesis. Cell Reports Sustainability. https://doi.org/10.1016/j.crsus.2024.100132

50. Suresh, H., & Guttag, J. (2021). A framework for understanding sources of harm throughout the machine learning life cycle. Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization, 1–9. https://doi.org/10.1145/3465416.3483305

51. van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

52. Vatamanu, A. F., & Tofan, M. (2025). Integrating Artificial Intelligence into Public Administration: Challenges and Vulnerabilities. Administrative Sciences, 15(4). https://doi.org/10.3390/admsci15040149

53. Veale, M., & Binns, R. (2017). Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data. Big Data & Society, 4(2), 2053951717743530. https://doi.org/10.1177/2053951717743530

54. Veale, M., & Brass, I. (2019). Administration by Algorithm? Public Management Meets Public Sector Machine Learning. Oxford Academic. https://doi.org/10.1093/oso/9780198838494.003.0006

55. Veale, M., Van Kleek, M., & Binns, R. (2018). Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3173574.3174014

56. Whitfield, S., & Hofmann, M. A. (2023). Elicit: AI literature review research assistant. Public Services Quarterly, 19(3), 201-207. https://doi.org/10.1080/15228959.2023.2224125

57. Wirtz, B. W., & Müller, W. M. (2019). An integrated artificial intelligence framework for public management. Public Management Review, 21(7), 1076–1100. https://doi.org/10.1080/14719037.2018.1549268

58. Wirtz, B. W., Langer, P. F., & Fenner, C. (2021). Artificial Intelligence in the Public Sector - a Research Agenda. International Journal of Public Administration, 44(13), 1103-1128. https://doi.org/10.1080/01900692.2021.1947319

59. Zang, J., & You, P. (2023). An industrial IoT-enabled smart healthcare system using big data mining and machine learning. Wireless Networks, 29(2), 909-918. https://doi.org/10.1007/s11276-022-03129-z

60. Zhang, D., Pee, L. G., Pan, S. L., & Cui, L. (2022). Big data analytics, resource orchestration, and digital sustainability: A case study of smart city development. Government Information Quarterly, 39(1), 101626. https://doi.org/https://doi.org/10.1016/j.giq.2021.101626

61. Žliobaitė, I. (2017). Measuring discrimination in algorithmic decision making. Data Mining and Knowledge Discovery, 31(4), 1060–1089. https://doi.org/10.1007/ s10618-017-0506-1