Artificial Intelligence Human Resource Management Algorithms for Employee Recruitment, Engagement, and Retention

This article reviews and advances existing literature concerning employee selection, recruitment, on-boarding, and monitoring across machine and deep learning-based collaborative working environments. In this research, previous findings were cumulated showing that labor-displacing artificial intelligence and automation technologies can be harnessed in machine learning-based occupational task composition, rising technological unemployment, workforce skill measurement, and job training and performance, and the contribution to the literature is by indicating that machine and deep learning video-based interviewing and algorithm-driven human resource management system design and development are instrumental in candidate attribute identification, organizational decision-making task optimization, and human resource operation interpretation and contextualization. Throughout February 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including "artificial intelligence human resource management algorithms" + "employee recruitment," "employee engagement," and "employee retention." As research published in 2023 was inspected, only 123 articles satisfied the eligibility criteria, and 10 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AXIS, MMAT, ROBIS, and SRDR. JEL codes: E24; J21; J54; J64 Keywords: artificial intelligence; human resource management algorithm; employee recruitment, engagement, and retention