Leveraging vulnerable and minority employees poses persistent challenges, underscoring the importance of artificial intelligence (AI) in the workplace, which is reshaping how employees are recruited and empowered. AI appropriation processes and the pursuit of efficiency raise profound concerns about equity and employee well-being. To provide a foundation for future research, this study systematically reviews the emerging literature on AI and its impact on vulnerable and minority employees, employing a combined bibliometric analysis, principal component analysis (PCA), and thematic synthesis. This analysis of the extant body of work reveals five overarching themes: (1) AI adoption and workforce transformation, (2) bias, equity, and fairness in AI systems, (3) employee well-being, inclusion, and empowerment, (4) ethical, legal, and governance considerations, and (5) methodological approaches and tools. Across these streams, this study highlights how theoretical perspectives, ranging from congruity theory and social role theory to feminist design thinking, the diversity, equity, and inclusion (DEI) framework, and Procedural Justice, have been applied to interpret AI’s organisational consequences. At the same time, empirical contributions remain fragmented and uneven. This review contributes to the literature by systematically mapping how AI intersects with the experiences of marginalised employees, identifying both the risks of algorithmic exclusion and the opportunities for designing more equitable work practices. Specifically, this review contributes to the information systems literature by synthesising how AI-enabled decision systems influence equity, inclusion, and empowerment outcomes for vulnerable and minority employees. By integrating insights from organisational studies, AI ethics, and digital workplace research, the study provides a structured framework explaining how algorithmic decision systems interact with organisational governance and workforce practices.
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Post Raj Pokharel (2026) studied this question.
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