Practical and Ethical Perspectives on AI-Based Employee Performance Evaluation

Scott Nicholas Pletcher

No 29yej, OSF Preprints from Center for Open Science

Abstract: For most, job performance evaluations are often just another expected part of the employee experience. While these evaluations take on different forms depending on the occupation, the usual objective is to align the employee’s activities with the values and objectives of the greater organization. Of course, pursuing this objective involves a whole host of complex skills and abilities which sometimes pose challenges to leaders and organizations. Automation has long been a favored tool of businesses to help bring consistency, efficiency, and accuracy to various processes, including many human capital management processes. Recent improvements in artificial intelligence (AI) approaches have enabled new options for its use in the HCM space. One such use case is assisting leaders in evaluating their employees’ performance. While using technology to measure and evaluate worker production is not novel, the potential now exists through AI algorithms to delve beyond just piece-meal work and make inferences about an employee’s economic impact, emotional state, aptitude for leadership and the likelihood of leaving. Many organizations are eager to use these tools, potentially saving time and money, and are keen on removing bias or inconsistency humans can introduce in the employee evaluation process. However, these AI models often consist of large, complex neural networks where transparency and explainability are not easily achieved. These black-box systems might do a reasonable job, but what are the implications of faceless algorithms making life-changing decisions for employees?

Date: 2023-04-28
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-hrm
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:29yej

DOI: 10.31219/osf.io/29yej

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