Who Gets Hired by the Algorithm? A Narrative Review of AI Fairness, Accountability, and Transparency in Hiring

Abstract

In recent years, organizations have raced to adopt AI tools in their HRM operations, particularly in recruitment and selection, in an attempt to benefit from this revolutionary technology by making hiring faster, more efficient, and more standardized. Although this has led to growing academic interest in AI hiring, the existing literature remains fragmented as many studies focus mainly on algorithmic bias and fairness, while little attention has been given to understanding how other dimensions, such as transparency and accountability, interact with fairness and with one another. To address this gap, this research reviews the existing literature on AI hiring in order to examine how bias, ethical concerns, and mitigation strategies are discussed through the FAT framework. The adoption of the FAT framework in this study is particularly important, as it provides a more integrated understanding of how fairness, transparency, and accountability interact and shape the design and use of AI hiring systems. Using a narrative literature review methodology, this study reviews 19 peer-reviewed articles published over the last 10 years and selected based on their relevance to bias, ethical concerns, and governance in AI hiring. By synthesizing the reviewed studies, the findings show that bias in AI hiring is layered, that ethical concerns extend beyond bias alone, and that organizations need to adopt a combination of strategies in order to address these concerns. The study also proposes practical recommendations for HR practitioners to support a more ethical and responsible use of AI hiring tools, and calls for future empirical research to examine AI hiring across different industries, cultures, and regions, particularly in the MENA context.