Human and algorithmic decision-making in the personnel selection process: A comparative bibliometric on bias

Abstract

This article examines, with bibliometrics, the publication on bias in organizations’ personnel selection processes, whether they use automated decision-making systems or human-made decisions. While human bias is dynamic, restricted, mutate, and easier to determine the source; algorithmic bias is large-scale, static, and unpredictable. Despite the apparent discrepancy, there is a symbiotic relationship between those two, but somehow only one of them is getting any attention regarding the consequences of fairness on personnel selection and how this influences organizational diversity. So, looking for a better understanding of organizational behaviour, we conduct a bibliometric review to mappings the relations of these two. Here we reviewed 55 articles from the Web of Science Core Collection, from the earliest research published in 1979 to 2021. Only papers of the document type “article” was considered. The tool used for bibliometric data analysis were bibliometrix packages from the RStudio system version 3.6.3. According to our review, the number of studies on the subject is still tiny, and most of them were conducted under controlled conditions without considering the error agent of an organizational environment such as time, organizational culture, and the emotions of the recruiter; this makes it impossible to develop practices to avoid discrimination in these spaces. Concerning the theme, studies on human bias are the most common, with a focus on gender bias, and have recently adopted diversity. Hardly studies on algorithm decision-making consider the process’s fairness as a topic for investigation. However, neither study demonstrates a correlation or systematic approach between them. More interdisciplinary and empirical research should be the focus of future studies.