Artificial intelligence subjectivity in the implementation of public administration: the cybernetic aspect

This paper examines the phenomenon of artificial intelligence subjectivity along with its cybernetic aspects. It explores various approaches to understanding and defining AI, starting with analyses of how the behavior of intelligent systems corresponds to human behavior patterns and extending to questions regarding the rationality of these systems’ actions. The works of N. Wiener, the founder of cybernetics, play a crucial role, as he established fundamental analogies between the functioning of living organisms and computational machines. These analogies are essential for understanding the nature of AI and its potential capabilities. The authors of this paper give particular attention to the functioning of AI and to discussions of its ability for self-learning and selfreproduction. The paper highlights AI's potential for self-development and adaptation to diverse conditions — these two features make it a key tool in modern technological innovation. It also analyzes the philosophical and sociological concept of AI subjectivity, suggesting that such systems may possess intelligence that significantly surpasses human cognitive abilities, along with the capacity for autonomous learning and expansion. Building on Wiener’s ideas, humanity must determine the nature of interaction between humans and machines (including AI) and organize the distribution of functions between them optimally. The authors conclude that the effectiveness of public administration can be enhanced through a comprehensive approach to implementing new information and technological solutions. It is about using neural networks and AI systems, providing qualified specialists and legal regulatory mechanisms for public authorities, and considering sociocultural and psycho-emotional factors, along with technical equipment and methods of logical-semantic interpretation.

artificial intelligence, cybernetics, subjectivity, legal personality, self-learning, self-replication, nervous system, cognitive functions