Abstract
The article analyzes the development of the labor market with the active integration of artificial intelligence methods in various areas of human activity: from light industry to the public sector. A hierarchical structure of artificial intelligence methods is proposed, in which emphasis is placed on machine learning and the introduction of robots for everyday and routine tasks. A predictive model of the development of AI methods with a cumulative effect is presented, in the context of which the issues of creating and washing professions from the labor market are discussed: professions that are under maximum impact, for example, specialists performing monotonous operations, are noted. Attention is paid in detail to the development of an AI model for making decisions while reducing the number of employees, since now many enterprises are prone to excessive crowding out of the workforce after the introduction of a number of AI-based solutions. The model allows not only to estimate the amount of staff reduction, but also makes it possible to select the most suitable employees for their further retraining. In developing the model, attention was also paid to recommendations for selecting features for forecasting and selecting the optimal scheme for adapting the model to various types of enterprises.
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This paper was prepared under financial support of the Russian Science Foundation (Grant No. 18-18-00099).
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Zhak, R., Kolesov, D., Leitão, J., Akaev, B. (2021). Uncertainty Decision Making Model: The Evolution of Artificial Intelligence and Staff Reduction. In: Schaumburg, H., Korablev, V., Ungvari, L. (eds) Technological Transformation: A New Role For Human, Machines And Management. TT 2020. Lecture Notes in Networks and Systems, vol 157. Springer, Cham. https://doi.org/10.1007/978-3-030-64430-7_5
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