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Using Artificial Intelligence to Evaluate Employees: The Effects on Recruitment, Effort, and Retention

Jason Brown et al.

Journal of Management Accounting Research2025https://doi.org/10.2308/jmar-2024-009article
AJG 2ABDC A*
Weight
0.41

Abstract

We use multiple experiments to explore the effects of artificial intelligence (AI) in employee evaluations and whether these effects differ based on employee demographics. Experiment 1 investigates whether having AI—as opposed to a White male manager—conduct employee performance evaluations affects firms’ recruiting efforts. We find that AI evaluators appear equally attractive to prospective employees from different demographic groups, whereas White male evaluators appear less attractive to under-represented minorities. Experiment 2 examines how current employees respond to receiving negative performance evaluations. We find that, after a negative performance evaluation, under-represented minorities provide less effort compared with White males under White male evaluators, but this difference is eliminated under AI evaluators. Collectively, our results provide evidence of costs and benefits related to the use of AI in performance evaluations and that, overall, this use differentially affects employees of different demographics. Data Availability: Data are available upon request. JEL Classifications: M12; M14; M41; M52.

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https://doi.org/https://doi.org/10.2308/jmar-2024-009

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@article{jason2025,
  title        = {{Using Artificial Intelligence to Evaluate Employees: The Effects on Recruitment, Effort, and Retention}},
  author       = {Jason Brown et al.},
  journal      = {Journal of Management Accounting Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/jmar-2024-009},
}

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Using Artificial Intelligence to Evaluate Employees: The Effects on Recruitment, Effort, and Retention

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.