Применение алгоритмов машинного обучения для сокращения информационной асимметрии и прогнозирования социальных изменений на рынке труда: вызовы и возможности

Abstract

This paper explores the impact of machine learning (ML) algorithms on reducing information asymmetry in the labor market and forecasting social changes, presenting challenges and opportunities for employers and job seekers alike. It addresses the issues of information asymmetry stemming from disparities in data access between labor market participants, as well as variations in the quality of information obtained, complicating the hiring and job search processes. Theoretical foundations of big data and its analysis using ML are discussed for identifying trends and patterns conducive to more efficient labor market functioning. ML methods including predictive models, clustering, classification, and text analysis are presented, describing their application in reducing information asymmetry and adapting to changing market demands. Examples of ML algorithm usage in real business processes are provided, showcasing their contribution to optimizing recruitment and personnel management processes, as well as forecasting future trends regarding significant competencies and skills. The importance of adapting to new challenges and opportunities presented by ML algorithms for maintaining competitiveness and sustainable development in the labor market is emphasized. By leveraging big data analytics, employers and job seekers can make more informed decisions, leading to more efficient labor market outcomes. However, challenges such as data privacy and algorithmic bias must be addressed to fully realize the benefits of ML in the labor market.