Machine learning applications to personnel selection: Current illustrations, lessons learned, and future research

Abstract Machine learning (ML) may be the biggest innovative force in personnel selection since the invention of employment tests. As such, the purpose of this special issue was to draw out research from applied settings to supplement the work that appeared in academic journals. In this overview article, we aim to complement the special issue in five ways: (1) provide a brief tutorial on some ML concepts and illustrate the potential applications in selection, along with their strengths and weaknesses; (2) summarize findings of the four articles in the special issue and provide an independent appraisal of the strength of the evidence; (3) identify some of the less‐obvious lessons learned and other insights that researchers new to ML might not clearly recognize from reading the special issue; (4) present best practices at this stage of the knowledge in selection; and (5) propose recommendations for future needed research based on the articles in the special issue and the current state of the science.