Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
Uncertainty decision making model : The evolution of artificial intelligence and staff reduction. / Zhak, Roman; Kolesov, Dmitrii; Leitão, Joao; Akaev, Bakytbek.
Technological Transformation: A New Role For Human, Machines And Management - TT-2020. ed. / Hanno Schaumburg; Vadim Korablev; Ungvari Laszlo. Springer Nature, 2021. p. 48-56 (Lecture Notes in Networks and Systems; Vol. 157).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Research › peer-review
}
TY - GEN
T1 - Uncertainty decision making model
T2 - 5th International Conference on Technological Transformation: A New Role for Human, Machines and Management, TT 2020
AU - Zhak, Roman
AU - Kolesov, Dmitrii
AU - Leitão, Joao
AU - Akaev, Bakytbek
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Data science
KW - Decision trees
KW - Deep learning
KW - Gradient boosting
KW - Labor market
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85098249983&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/5f4a7444-f76b-36ea-99ee-46c047cd0cf6/
U2 - 10.1007/978-3-030-64430-7_5
DO - 10.1007/978-3-030-64430-7_5
M3 - Conference contribution
AN - SCOPUS:85098249983
SN - 9783030644291
T3 - Lecture Notes in Networks and Systems
SP - 48
EP - 56
BT - Technological Transformation
A2 - Schaumburg, Hanno
A2 - Korablev, Vadim
A2 - Laszlo, Ungvari
PB - Springer Nature
Y2 - 16 September 2020 through 18 September 2020
ER -
ID: 76593635