Big data and artificial intelligence in public administration: opportunities and risks

The article is devoted to the analysis of the opportunities and risks of using big data and artificial intelligence (AI) in public administration. The author examines the concept of big data, its key characteristics (volume, speed, diversity, reliability) and ways to process them using AI. The main focus is on the use of AI systems in areas such as forecasting socio-economic development, healthcare, education, combating crime and providing public services. Examples of successful AI implementation in different countries (Singapore, USA, China, Russia, Great Britain, South Korea) are considered. The risks associated with digital bias, cybercrime, privacy interference, social injustice, and the disintegration of social ties are also analyzed. The author emphasizes the importance of regulating the use of AI in the public and corporate sectors, including the legal responsibility of developers, corporations, and government agencies for biased algorithmic decisions, as well as the implementation of ethical standards and user feedback mechanisms. The research methodology includes an expert survey using an online spreadsheet on the Google platform, which allowed 24 representatives of the Russian academic community to be surveyed during October–December 2024. The respondents were leading Russian experts from 16 Russian federal universities representing all 8 federal districts (3 experts from each district). The scientific novelty of the research lies in the systematization of the risks associated with the introduction of big data and AI in public administration, based on an expert survey of leading Russian specialists from 8 federal districts. For the first time, a ranked classification of risks has been proposed, including digital bias, non-transparency of AI solutions, threat to personal data, and possible intensification of social injustice. The article's conclusions emphasize the need to regulate AI algorithms, implement ethical standards, and develop mechanisms to protect citizens from discriminatory decisions. The author emphasizes the importance of testing algorithms for bias, corporate responsibility of developers, and user feedback. The study contributes to the development of the concept of digital public administration, demonstrating a balance between technological capabilities and potential risks from the introduction of AI and big data.

Big Data, Artificial Intelligence (AI), Public Administration, Machine Learning, Forecasting, Cybercrime, Digital Bias, Personal Data, Automation, Ethical Standards