NEURAL NETWORK RESPONSES TO USER REQUESTS: INTERPRETATION AND PERCEPTION FEATURES

The article explores the interaction of users with neural networks in a political context, focusing on their influence on the formation of public opinion and the interpretation of political events. The main purpose of the work is to identify the factors that determine the perception of neural network responses, including cognitive characteristics, cultural context, and the level of trust in technology. The study shows that despite the high functionality of neural networks, there are a number of problems associated with their adaptation to the audience, such as the bias of algorithms and insufficient transparency in response formation. Furthermore, this study pays special attention to the issue of user trust in neural network algorithms. The work offers recommendations for improving the efficiency of neural networks, including the development of adaptive algorithms and improving training data. In conclusion, important political challenges related to the manipulation of public opinion, the ethical responsibility of politicians, distrust of information, digital inequality, and threats to national security are highlighted. The problems identified emphasize the need for an interdisciplinary approach and the development of regulatory strategies that ensure the safe and ethical use of neural networks in public and political life, which is key to their effective use in the future.