Abstract

AI-based recruitment offers significant efficiency and effectiveness in talent acquisition, but it also introduces algorithmic bias, which can hinder fairness. Despite the proliferation of studies on AI-based recruitment, little is known about what and how managerial approaches have been applied to ensure fairness. This scoping review applies the typology of augmentation for fairness as managerial approaches in AI-based recruitment: reactive oversight, proactive oversight, informed reliance, and supervised reliance. Additionally, a multilevel perspective is adopted to examine these approaches within organisational dynamics. We synthesise 47 studies and find that 46 of them position humans as the final decision-makers, indicating users’ awareness of AI’s limitations and the emergence of augmenting fairness. However, human biases in informed decision-making (supervised reliance) remain overlooked, with limited attention given to group and organisational levels. Based on these findings, we propose a research agenda in Information Systems (IS) and offer practical implications to advance fair hiring practices.

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