Improving measurement and prediction in personnel selection through the application of machine learning

Nick Koenig, Scott Tonidandel, Isaac Thompson, Betsy Albritton, Farshad Koohifar, Georgi Yankov, Andrew Speer, Jay H. Hardy, Carter Gibson, Chris Frost, Mengqiao Liu, Denver McNeney, John Capman, Shane Lowery, Matthew Kitching, Anjali Nimbkar, Anthony Boyce, Tianjun Sun, Feng Guo, Hanyi MinBo Zhang, Logan Lebanoff, Henry Phillips, Charles Newton

Research output: Contribution to journalArticlepeer-review


Machine learning (ML) is being widely adopted by organizations to assist in selecting personnel, commonly by scoring narrative information or by eliminating the inefficiencies of human scoring. This combined article presents six such efforts from operational selection systems in actual organizations. The findings show that ML can score narrative information collected from candidates either in writing or orally in response to assessment questions (called constructed response) as accurately and reliably as human judges, but much more efficiently, making such responses more feasible to include in personnel selection and often improving validity with little or no adverse impact. Moreover, algorithms can generalize across assessment questions, and algorithms can be created to predict multiple outcomes simultaneously (e.g., productivity and turnover). ML has even been demonstrated to make job analysis more efficient by determining knowledge and skill requirements based on job descriptions. Collectively, the studies in this article illustrate the likely major impact that ML will have on the practice and science of personnel selection from this point forward.

Original languageEnglish (US)
Pages (from-to)1061-1123
Number of pages63
JournalPersonnel Psychology
Issue number4
StatePublished - Dec 1 2023


  • artificial intelligence/big data/machine learning
  • selection-methods
  • selection-validation

ASJC Scopus subject areas

  • Applied Psychology
  • Organizational Behavior and Human Resource Management


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