Share:


The ways of introducing AI/ML-based prediction methods for the improvement of the system of government socio-economic administration in Ukraine

Abstract

The objective of the article is to develop and test in practice a mechanism for constructing AI/ML-based predictions, adapted for use in the system of government socio-economic administration in Ukraine. Research design is represented by several methods like qualitative analysis in order to identify potential benefits of AI use in different spheres of government administration, synthesis to generate new datasets for the experiment, and abstraction to abstract from the current situation in Ukraine, population displacement, uneven statistics reporting. Among empirical methods are prediction and experimental methods to construct a mechanism for the implementation of AI/ML prediction methods in public administration, develop a high-level architecture of the AI/ML prediction system, and create and train the COVID-19 prediction neuron network. A holistic vision of the AI/ML-based prediction construction mechanism, depending on data taken from state official online platforms, is presented, in addition, the ways of its possible practical application for the improvement of the national system of state socio-economic administration are described. The main condition and guarantee of obtaining accurate results is access to quality data through platforms such as Diia, HELSI, national education platforms, government banks, etc. The findings of the research suggest that wide implementation of AI/ML-based prediction technologies will allow the government in perspective to increase the efficiency of the use of budgetary resources, the effectiveness of the government target programs, improve the quality of public administration and to better satisfy the citizens’ demand. Future studies should be done to overcome the limitations of the approach: find a way to protect and extract sensitive information from government platforms, fight neural network bias, and create a more perfect system that is able to make multiparameter predictions and is also self-improving on the basis of the obtained results.

Keyword : public administration, a system of socio-economic regulation, AI/ML-based prediction methods, neural networks, enterprise, business model

How to Cite
Ivashchenko, T., Ivashchenko, A., & Vasylets, N. (2023). The ways of introducing AI/ML-based prediction methods for the improvement of the system of government socio-economic administration in Ukraine. Business: Theory and Practice, 24(2), 522–532. https://doi.org/10.3846/btp.2023.18733
Published in Issue
Nov 21, 2023
Abstract Views
396
PDF Downloads
284
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abillama, N., Mills, S., Boison, G., & Carrasco, M. (2021). Unlocking the value of AI-powered government. Boston Consulting Group. https://web-assets.bcg.com/27/58/3f8a469e45d2ad01c74d3ba15f7d/bcg-unlocking-the-value-of-ai-powered-government-july-2021.pdf

Anandhanathan, P., & Gopalan, P. (2021). Comparison of machine learning algorithm for COVID-19 death risk prediction. Research Square. https://doi.org/10.21203/rs.3.rs-196077/v1

Biz Cenzor. (2022, May 24). The number of participants in the addendum “Diia” exceeded 17 million. https://biz.censor.net/news/3343506/kilkist_korystuvachiv_dodatku_diya_perevyschyla_17_milyioniv_mintsyfry

Bokonda, L., Ouazzani-Touhami, K., & Souissi, N. (2020). Predictive analysis using machine learning: Review of trends and methods. In 2020 International Symposium on Advanced Electrical and Communication Technologies (ISAECT). IEEE. https://doi.org/10.1109/ISAECT50560.2020.9523703

Buerkli, D., & Gagliani, M. (2018, October 30). How to make AI work in government and for people (Report). Centre for Public Impact. A BCG Foundation. https://www.centreforpublicimpact.org/assets/documents/CPI-How-to-make-AI-work-in-government-and-for-people.pdf

Cabinet of Ministers of Ukraine. (2020, December). Concept for the Development of Artificial Intelligence in Ukraine December 2, 2020, No. 1556-p. https://zakon.rada.gov.ua/laws/show/1556-2020-%D1%80#n8

Castelli, M., Manzoni, L., & Popovic, A. (2016). An artificial intelligence system to predict quality of service in banking organizations. Computational Intelligence and Neuroscience, 2016(4), 1–7. https://doi.org/10.1155/2016/9139380

Daub, M., Domeyer, A., Lamaa, A., & Renz, F. (2020, July 15). Digital public services: How to achieve fast transformation at scale. McKinsey and Company. https://www.mckinsey.com/industries/public-and-social-sector/our-insights/digital-public-services-how-to-achieve-fast-transformation-at-scale

Desouza, K. C. (2018). Delivering artificial intelligence in government: Challenges and opportunities. IBM Center for The Business of Government. Arizona State University. https://www.businessofgovernment.org/sites/default/files/Delivering%20Artificial%20Intelligence%20in%20Government.pdf

Dhasarathy, A., Jain, S., & Khan, N. (2020, October 19). When governments turn to AI: Algorithms, trade-offs, and trust. McKinsey and Company. https://www.mckinsey.com/industries/public-and-social-sector/our-insights/when-governments-turn-to-ai-algorithms-trade-offs-and-trust

Ebhuoma, O., Gebreslasie, M., & Magubane, L. (2018). A seasonal autoregressive integrated moving average (SARIMA) forecasting model to predict monthly malaria cases in KwaZulu-Natal, South Africa. South African Medical Journal, 108(7), 573–578. https://doi.org/10.7196/SAMJ.2018.v108i7.12885

Eggers, W., Schatsky, D., & Viechnicki, P. (2017, 26 April). AI-augmented government. Using cognitive technologies to redesign public sector work. Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/cognitive-technologies/artificial-intelligence-government.html

Farooq, J., & Bazaz, M. A. (2021) A deep learning algorithm for modeling and forecasting of COVID-19 in five worst affected states of India. Alexandria Engineering Journal, 60(1), 587–596. https://doi.org/10.1016/j.aej.2020.09.037

Fejes, E., & Futo, I. (2021). Artificial intelligence in public administration – supporting administrative decisions. Public Finance Quarterly, State Audit Office of Hungary, 66(5), 23–51. https://doi.org/10.35551/PFQ_2021_s_1_2

Karpenko, O., & Karpenko, Y. (2021). Artificial intelligence as a tool of public administration of socioeconomic development: Smart infrastructure, digital business analysis and transfer system. Derzhavne upravlinnya: udoskonalennya ta rozvytok, 10. https://doi.org/10.32702/2307-2156-2021.10.2

Kaur, M., Buisman, H., Bekker, A., & McCulloch, C. (2022). Innovative capacity of governments: A systemic framework. OECD Working Papers on Public Governance, 51, 1–42. https://doi.org/10.1787/52389006-en

Kosorukov, A. A. (2019). Artificial intelligence technologies in modern public administration. Sociodynamics, 5, 43–58. https://doi.org/10.25136/2409-7144.2019.5.29714

Kouziokas, G. N., Chatzigeorgiou A., & Perakis K. (2017). Artificial intelligence and regression analysis in predicting ground water levels in public administration. European Water, 57, 362–367.

Kouziokas, G. N. (2017). The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment. Transportation Research Procedia, 24, 467–473. https://doi.org/10.1016/j.trpro.2017.05.083

Kvitka, S., Novichenko, N., & Bardakh, O. (2021). Artificial intelligence in municipal administration: Vectors of development. Public Administration Aspects, 9(4), 85–94. https://doi.org/10.15421/152140

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), 1–18. https://doi.org/10.1016/j.giq.2022.101774

Mishra, A. (2020). Machine learning classification models for detection of the fracture location in dissimilar friction stir welded joint. Applied Engineering Letters. Journal of Engineering and Applied Sciences, 5(3), 87–93. https://doi.org/10.18485/aeletters.2020.5.3.3

Operational Data Portal. (2023, February 15). Ukraine Refugee situation. https://data.unhcr.org/en/situations/ukraine

Osman, N., Torki, M., ElNainay, M., AlHaidari, A., & Nabil, E. (2021). Artificial intelligence-based model for predicting the effect of governments’ measures on community mobility. Alexandria Engineering Journal, 60(4), 3679–3692. https://doi.org/10.1016/j.aej.2021.02.029

Pencheva, I., Esteve, M., & Mikhaylov, S. J. (2020). Big Data and AI – A transformational shift for government: So, what next for research? Public Policy and Administration, 35(1), 24–44. https://doi.org/10.1177/0952076718780537

Perricos, C., & Kapur, V. (2020). Anticipatory government. Preempting problems through predictive analytics. Government trends 2020. Deloitte insights. https://www2.deloitte.com/content/dam/Deloitte/lu/Documents/public-sector/lu-government-trends-2020.pdf

Pettit, R. W., Fullem, R., Cheng, Ch., Amos, & Ch. I. (2021). Artificial intelligence, machine learning, and deep learning for clinical outcome prediction. Emerging Topics in Life Sciences, 5(6), 729–745. https://doi.org/10.1042/ETLS20210246

Pourhomayoun, M., & Shakibi, M. (2020). Predicting mortality risk in patients with COVID-19 using artificial intelligence to help medical decision-making. medRxiv. https://doi.org/10.1101/2020.03.30.20047308

Pytorch documentation. (2023, March 8). LSTM algorithm. https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x

Siahaan, V., & Sianipar, R. H. (2021). COVID-19: Analysis, classification, and detection using Scikit-Learn, Keras, and TensorFlow with Python GUI. Balige Publishing.

The Center for Systems Science and Engineering. (2023, February). COVID-19 Data repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. https://github.com/CSSEGISandData/COVID-19

Tito, J., & Croisier, S. (2017). Analysing AI: The impact of artificial intelligence on government. Centre for public impact. A BCG Foundation. https://www.centreforpublicimpact.org/insights/analysing-ai-impact-artificial-intelligence-ai-government

van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., McKinney, Jarrett, D., Lio, P., & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110, 1–14. https://doi.org/10.1007/s10994-020-05928-x

Wakefield, K. (2023, March 8). Predictive modelling analytics and machine learning. https://www.sas.com/en_gb/insights/articles/analytics/a-guide-to-predictive-analytics-and-machine-learning.html

Yaseen, Z. M., Ali, Z. H., Salih, S. Q., & Al-Ansari, N. (2020). Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability, 12(4), 1514. https://doi.org/10.3390/su12041514

Zuiderwijk, A., Chen, Y., & Salem, F. (2021). Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Government Information Quarterly, 38(3), 1–19. https://doi.org/10.1016/j.giq.2021.101577