Impact of machine learning on personnel selection

Abstract

The purpose of this article is to describe the impact of artificial intelligence (AI), and specifically Machine Learning (ML) and Natural Language Processing (NLP), on personnel selection in terms of potential uses, challenges for practice, and recommendations based on the most recent advances in the science. We argue that ML will likely have as big of an influence on hiring procedures as the equal employment laws did in the 1960s, 1970s, and 1980s. We start by describing why personnel selection is an obvious application of ML, followed by a brief definition of the types of ML and key terms. In the first section, we describe the most common currently known uses of ML in personnel selection, along with a brief summary of the scientific evidence supporting the uses and potential pros and cons. In the second section, we describe the challenges and issues managers will face in using ML in selection and provide some preliminary advice as to how to address them. Challenges include the influence on adverse impact against diversity subgroups of candidates, explainability of the algorithms, validation and legal defensibility, new emerging state laws governing AI, the potential use of AI tools by candidates, likely future developments, and whether to make or buy should organizations decide to pursue ML for selection. We end with a set of recommendations for managers, concluding that the choice is probably when, rather than if, to adopt ML in personnel selection.