Machine Learning Applied in Personnel Selection Processes: A Systematic Review

Garcia B.; Tello A.; Ballero R.; Quiroz I. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/laccei2024.1.1.960

Resumen

The increasing use of Artificial Intelligence (AI) has led companies to incorporate it into their processes to improve efficiency. This approach has not yet become widespread; Machine Learning stands out among AI techniques in the personnel selection process to address frequent problems in this process and enhance performance. This review aims to analyze the effectiveness of Machine Learning in recruitment to reduce costs and uncertainty in decision-making that have been presented using traditional interview methods. To this end, a systematic review without meta-analysis was conducted, using the PICOC methodology to define the components that will guide the review, and PRISMA was used to select articles based on established inclusion and exclusion criteria, from which 20 articles from the Scopus and Redalyc databases were selected. The results identified that 90% of authors agree that employing Machine Learning techniques in the recruitment process has a positive impact, significantly improving efficiency and reducing existing problems in the traditional methodology. It was also found that the stages of analysis, interview, and decision-making in the recruitment process are primarily where Machine Learning implementation occurs. Therefore, it was concluded that the use of Machine Learning greatly improves the recruitment process and supports cost reduction and uncertainty through automation. Thus, it is an approach that companies should adopt to leverage all the benefits provided by this technological trend.

Instituciones: Universidad Tecnológica del Perú