Machine Learning applied in personnel selection processes: A systematic review.

Abstract

The increasing use of Artificial Intelligence (AI) has led companies to incorporate it into their processes to improve efficiency. This approach is not yet widespread, Machine Learning stands out among AI techniques for the recruitment process to address common problems in this process and improve performance. This review seeks to analyze the effectiveness of Machine Learning in recruitment to reduce costs and uncertainty in decision making that have arisen using traditional interviewing methods. For this purpose, a systematic review without meta-analysis was carried out, using the PICOC methodology to define the components that will guide the review and PRISMA was used to select the articles based on the established inclusion and exclusion criteria, of which 20 articles from the Scopus and Redalyc databases were selected. The results identified that 90% of authors agree that the use of Machine Learning techniques in the recruitment process has a positive impact in terms of significantly improving efficiency and reducing problems existing in the traditional methodology. Also, it was found that the analysis, interview and decision making stages of the recruitment process are where the implementation of Machine Learning mainly falls. This led to the conclusion that the use of Machine Learning greatly improves the recruitment process and supports the reduction of costs and uncertainty through automation. Therefore, it is an approach that companies should adopt in order to use all the benefits provided by this technological trend.