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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 study assesses how managers 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, we first investigate how individual characteristics of managers influence their perceived usefulness of algorithm selection information. We then examine how managers 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 and managers with experience using these systems perceived algorithm-based decision aids useful. The same relationships were found when managers rated employability of applicants using information from both resumes and algorithm-based decision aids. Managers were less likely to see algorithm-based information as useful if they reported algorithm aversion, but no less likely to use the information when assessing candidates. Finally, we 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.
Chen et al. (Mon,) studied this question.