Towards Autonomous Digital Governance: Integrating AI, Data Governance, and Smart Infrastructure for Future Government
Keywords:
Autonomous Digital Governance, Artificial Intelligence, Data Governance, Smart Infrastructure, Public AdministrationAbstract
The rapid advancement of digital technologies has transformed public governance, evolving from e-government to more integrated digital government systems. However, the transition toward fully autonomous digital governance remains limited. This study aims to analyze how the integration of Artificial Intelligence (AI), data governance, and smart infrastructure can enable the development of autonomous digital governance systems. Using a mixed-method approach, this research combines a systematic literature review and case study analysis with quantitative survey data to examine the relationships between key variables, including AI capability, data governance quality, and infrastructure readiness. The findings indicate that the integration of these components significantly contributes to the formation of an autonomous decision-making system, which in turn enhances governance outcomes in terms of efficiency, transparency, and responsiveness. AI capability emerges as the most influential factor, particularly in enabling automation and predictive analytics, while data governance ensures the reliability and accountability of data-driven processes. Smart infrastructure supports real-time data collection and system connectivity, although disparities in infrastructure readiness remain a challenge. The study also identifies key benefits of autonomous digital governance, including faster decision-making, reduced human bias, and the development of predictive public services. However, several risks are highlighted, such as ethical concerns, privacy issues, and over-reliance on technology. This research proposes an integrated conceptual model of autonomous digital governance, emphasizing the need for synergy between technological and institutional components. The study contributes to the advancement of digital governance theory while providing practical insights for policymakers in designing future-ready governance systems.
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Al Batayneh, R. M., Taleb, N., Said, R. A., Alshurideh, M. T., Ghazal, T. M., & Alzoubi, H. M. (2021). IT governance framework and smart services integration for future development of Dubai infrastructure utilizing AI and big data, its reflection on the citizens standard of living. The International Conference on Artificial Intelligence and Computer Vision, 235–247.
Alam, M. K. (2021). A systematic qualitative case study: questions, data collection, NVivo analysis and saturation. Qualitative Research in Organizations and Management: An International Journal, 16(1), 1–31.
Bayaml?o?lu, E., & Leenes, R. (2018). The ‘rule of law’implications of data-driven decision-making: a techno-regulatory perspective. Law, Innovation and Technology, 10(2), 295–313.
Castleberry, A., & Nolen, A. (2018). Thematic analysis of qualitative research data: Is it as easy as it sounds? Currents in Pharmacy Teaching and Learning, 10(6), 807–815.
Dawes, S. S. (2008). The evolution and continuing challenges of e?governance. Public Administration Review, 68, S86–S102.
De Almeida, P. G. R., Dos Santos, C. D., & Farias, J. S. (2021). Artificial intelligence regulation: a framework for governance. Ethics and Information Technology, 23(3), 505–525.
Erkut, B. (2020). From digital government to digital governance: are we there yet? Sustainability, 12(3), 860.
Faruk, O. M., & Sultana, M. S. (2021). Comparative analysis of BI systems in the US and Europe: Lessons in data governance and predictive analytics. Journal of Sustainable Development and Policy, 1(5), 1–38.
Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., & Al-Fuqaha, A. (2017). Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys & Tutorials, 19(4), 2456–2501.
Greenspan, A. (2006). A framework for making a better decision. Research Review, 13(1), 1.
Hooda, A., & Singla, M. L. (2021). Core–competencies–a key to future–oriented and sustainable e-governance implementation: a mixed method research. Transforming Government: People, Process and Policy, 15(1), 80–107.
Jackson, P. M. (2001). Public sector added value: can bureaucracy deliver? Public Administration, 79(1), 5–28.
Keramati, A., Behmanesh, I., & Noori, H. (2018). Assessing the impact of readiness factors on e-government outcomes: An empirical investigation. Information Development, 34(3), 222–241.
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44(6), 101976.
Matheus, R., Janssen, M., & Maheshwari, D. (2020). Data science empowering the public: Data-driven dashboards for transparent and accountable decision-making in smart cities. Government Information Quarterly, 37(3), 101284.
Milakovich, M. E. (2012). Digital governance: New technologies for improving public service and participation. Routledge.
Ogunmokun, A. S., Balogun, E. D., & Ogunsola, K. O. (2021). A conceptual framework for AI-driven financial risk management and corporate governance optimization. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 781–790.
Parimi, S. K., & Yallavula, R. (2021). Data-Governed Autonomous Decisioning: AI Models for Real-Time Optimization of Enterprise Financial Journeys. International Journal of Emerging Trends in Computer Science and Information Technology, 2(1), 88–100.
Prada-Ramallal, G., Roque, F., Herdeiro, M. T., Takkouche, B., & Figueiras, A. (2018). Primary versus secondary source of data in observational studies and heterogeneity in meta-analyses of drug effects: a survey of major medical journals. BMC Medical Research Methodology, 18(1), 97.
Ravichandran, N., Inaganti, A. C., Muppalaneni, R., & Nersu, S. R. K. (2020). AI-Powered Workflow Optimization in IT Service Management: Enhancing Efficiency and Security. Artificial Intelligence and Machine Learning Review, 1(3), 10–26.
Sarwat, A. I., Sundararajan, A., Parvez, I., Moghaddami, M., & Moghadasi, A. (2018). Toward a smart city of interdependent critical infrastructure networks. In Sustainable interdependent networks: From theory to application (pp. 21–45). Springer.
Serrano, W. (2018). Digital systems in smart city and infrastructure: Digital as a service. Smart Cities, 1(1), 134–154.
Singh, H. (2019). Artificial intelligence for predictive analytics: Gaining actionable insights for better decision-making. International Journal of Research in Electronics and Computer Engineering, 8(1).
Tien, J. M. (2017). Internet of things, real-time decision making, and artificial intelligence. Annals of Data Science, 4(2), 149–178.
Werkhoven, P., Kester, L., & Neerincx, M. (2018). Telling autonomous systems what to do. Proceedings of the 36th European Conference on Cognitive Ergonomics, 1–8.
Yigitcanlar, T., Corchado, J. M., Mehmood, R., Li, R. Y. M., Mossberger, K., & Desouza, K. (2021). Responsible urban innovation with local government artificial intelligence (AI): A conceptual framework and research agenda. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 71.
Yu, H., Abdullah, A., & Saat, R. M. (2014). Overcoming time and ethical constraints in the qualitative data collection process: A case of information literacy research. Journal of Librarianship and Information Science, 46(3), 243–257.
Zhang, N., & Yuan, Q. (2016). An overview of data governance. Economics Paper, December.
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