Abstract

AI-based recruitment offers significant efficiency and effectiveness in talent acquisition but also introduces algorithmic bias that can perpetuate and amplify unfairness in hiring practices. In this scoping review, we extend the typology of augmentation for fairness as managerial approaches in AI-based recruitment: reactive oversight, proactive oversight, informed reliance, and supervised reliance. We also adopt a multilevel perspective (i.e., individual, group, and organisational levels) to examine these approaches within organisational dynamics. We synthesise and map 47 studies and find that 46 of them position humans as the final decision-makers, indicating users’ awareness of AI 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) to advance comprehensive and fair hiring practices, emphasising the importance of engagement across all organisational levels.

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