Resumo:
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.
