Artificial Intelligence (AI) in Employee Selection: How Algorithm-Based Decision Aids Influence Recruiters’ Decision-Making in Resume Screening

Abstract

With the development of artificial intelligence (AI), algorithm-based decision aids have been adopted by more and more organizations to help recruiters and hiring managers screen and review job candidates. This dissertation assesses how HR recruiters integrate selection information produced by algorithms into assessments of job candidates’ qualifications to make the hiring decisions. To assess how algorithm-based decision aids are used, I first investigate how individual characteristics of recruiters influence their perceived usefulness of algorithm selection information. I then examine how recruiters rate applicant employability when they are given different types of jobs (HR Assistant vs. Data Engineer) and algorithm-based selection information. Results showed that younger managers, managers with AI use experience and more recent hiring experience perceived algorithm-based decision aids useful. Recruiters were less likely to see algorithm-based information as useful if they reported algorithm aversion. Similar relationships were found when managers rated employability when presented with information from both resumes and algorithm-based decision aids. Finally, I found that applicant information from algorithm-based decision aids had more influence on manager ratings of employability when the job requires more technical skills than when the job requires more soft skills. Theoretical and empirical implications are discussed.