Detecção de insatisfação de servidores públicos com inteligência artificial

Abstract

This work highlights a comprehensive investigation into the application of Artificial Intelligence (A.I.) in human resources management, with a specific focus on identifying employee dissatisfaction through machine learning approaches. The research included a review of scientific articles discussing both the implementation of A.I. in the context of human resources and the use of machine learning techniques to detect cases of turnover/attrition, along with the relationship between dissatisfaction and turnover/attrition cases. To assess these approaches, four validated public databases were selected. Three of them contained fictional employee data, and one contained real employee turnover data. Each database underwent a process of textual field factorization, followed by analyses to highlight the data distributions in each set. In conducting the research, different machine learning approaches were applied to each of the databases, aiming to verify the feasibility of identifying dissatisfaction through A.I. The techniques used included anomaly or novelty detection, classifiers, and optimized sets of classifiers. The results were quantified, revealing promising scores, with performances exceeding 90%. These results emphasize the overall effectiveness of machine learning in identifying employee dissatisfaction, demonstrating its potential for practical applications in the human resources environment.