Abstract
Introduction. These days, the public sector digitalization stands as one of the most crucial global trends. Artificial intelligence (AI) emerges as a key technology within GovTech approach, and its application can significantly enhance the efficiency and quality of state and municipal services. Simultaneously, the rapid introduction of new technologies into such a sensitive sector is associated with risks that, under conditions of insufficiently developed risk management systems, can even undermine national security. This research aims at conducting a comprehensive analysis of the prospects for implementing AI solutions in the public sector within the GovTech context, and it is intended to study the key capabilities of this technology and the systemic risks accompanying them, as well as to develop a solution for their minimization.
Materials and methods. To achieve the set goal, a comprehensive methodology was applied, encompassing documentary analysis of scientific literature (published between 2022–2025), content analysis, and comparative analysis (of opportunities and the associated risks in AI implementation within GovTech). The final stage of the methodological process was the results processing into a holistic conceptual model. The choice of a qualitative methodology, rather than a quantitative one, was determined by the necessity of creating a holistic picture of the phenomenon. The article logically stems from the research previously published by the authors.
Results and conclusions. The results of the conducted research indicate the necessity of recognizing the fundamental importance of a risk management system when implementing AI solutions in the public sector and the unconditional priority of “Trusted AI” principles at all stages of the system lifecycle. During the research, the authors described the directions of AI application within GovTech across various spheres of public administration and presented a classification of risks. This allowed for the identification of a dialectical relationship between them and the determination of key problematic areas in risk management practices. The novelty of the research lies in conducting a comprehensive analysis of the synergies and contradictions of the public sector.
Discussion. The results present value in defining the importance of overcoming a narrowly focused approach and the actual implementation of the ethically sound “Trusted AI” concept, as well as integrating risk management into the core processes of AI development and implementation within GovTech approach.
References
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