The study arrives at a moment when AI-based literature has gained unprecedented traction, particularly since 2022, and when automated decision-making has spread across business functions from human resource management to customer service platforms. Vulnerable and minority employees frequently face systemic barriers that include discrimination, underrepresentation in leadership positions, limited access to career advancement, and insufficient organisational support. AI-based recruitment, performance evaluation, and career development systems promise to reduce these inequities by emphasising standardised, data-driven decisions that minimise the influence of unconscious human prejudice. The technology can also support inclusive job matching, skill development, and accessibility initiatives, empowering workers who have traditionally experienced disadvantage. Yet poorly designed or unmonitored systems risk creating feedback loops that systematically disadvantage the very groups they could help, as high-profile cases of recruitment algorithms reproducing gender and racial bias from biased historical data have demonstrated.

To map this contested terrain, the review employed an unusually rigorous multi-phase methodology. The author conducted a keyword-based literature search in the Scopus database on August 10, 2025, using a Boolean query that combined terms for artificial intelligence, machine learning, and algorithmic decision-making with terms covering vulnerable and minority employee populations, human resource management, and ethics. The initial search identified 554 articles; after excluding 308 records that were not the required publication types and 9 articles not in English, 237 articles entered bibliometric mapping and principal component analysis. A final manual thematic synthesis drew on 30 articles published in A*, A, and B-ranked journals according to the Australian Business Deans Council index. The full PRISMA-guided screening process was documented to ensure transparency, and all 237 articles were included in the statistical analyses to capture the interdisciplinary breadth of the field, spanning information systems, AI ethics, disability studies, and organisational behaviour.

The quantitative core of the review combined VOSviewer bibliometric mapping with principal component analysis. Keyword co-occurrence analysis identified 93 recurring keywords, and a Kaiser-Meyer-Olkin test of sampling adequacy, with a threshold of 0.50, filtered these down to 16 keywords for the final PCA. Applying Kaiser’s criterion of eigenvalues greater than 1, the analysis extracted six principal components, which the author labelled as social equity and representation, ethical governance and AI technology, inclusion and organisational adaptation, employment and knowledge transformation, structural challenges in workforce diversity, and goals and workplace realities. Scree plots illustrated the effect of the dimensionality reduction. These statistically derived components were then consolidated, through qualitative interpretation, into five overarching themes that structure the review’s synthesis: AI adoption and workforce transformation; bias, equity, and fairness in AI systems; employee well-being, inclusion, and empowerment; ethical, legal, and governance considerations; and methodological approaches and tools.

Publication trends reveal how young the field is. Minimal contributions appeared between 2006 and 2019, but a sharp upward trajectory began in 2020, with publications rising to six that year and eight by 2023. Knowledge Management Research and Practice leads the top ten journals by total citations with 305, followed by Informing Science with 183 and the Journal of Information, Communication and Ethics in Society with 167. The theoretical landscape underpinning the literature is rich but fragmented, and the review groups it into four domains: ethics, justice, and fairness; organisational and human resource management; technology and digital transformation; and disability, diversity, and inclusion frameworks. The first domain includes procedural justice, algorithmic fairness, and responsible AI innovation models. The second draws on the Resource-Based View, the Dynamic Capability Framework, and strategic human resource management perspectives. The third relies on technology adoption and algorithmic management theories, while the fourth deploys frameworks such as Disability Justice, Feminist Design Thinking, neurodiversity models, and identity-consciousness versus identity-blindness approaches that position marginalised employees not merely as subjects of algorithmic governance but as knowledge holders and co-designers of inclusive AI systems.

Among the review’s most striking empirical findings is evidence that AI-generated communications can provoke stronger negative reactions toward employees with disabilities or women than traditional human-based bias, exceeding it in some contexts. Research on construction and engineering leadership documents a likeability versus competency dilemma, in which women with comparable qualifications and experience are perceived as less likeable than male peers. In digital skills and STEM training programmes, recruiters using AI-based candidate screening were found to favour male candidates during initial outreach, especially under high workloads, demonstrating how such systems can reinforce gender-based disparities even before applications are submitted. Studies of AI-assisted disability assessments show that biases may emerge from design choices, data selection, or operational deployment, underscoring the importance of participatory, disability-led design practices. Meanwhile, profiling models in public employment services can inadvertently misclassify and discriminate against minority or foreign-origin jobseekers, exposing a sharp trade-off between accuracy and equity.

The review also documents AI’s genuine potential for empowerment. AI-driven innovation can enhance transparency, strengthen internal controls, and shape workplace culture and performance evaluation systems, while applications in policy evaluation, exemplified by China’s Low-Carbon City Pilot program, show how algorithmic tools can improve job quality, entrepreneurship opportunities, and urban labour inclusivity. Healthcare studies demonstrate that AI systems designed with clear reasoning, adaptive triage, and data transparency can reduce cognitive burdens while promoting equitable outcomes for diverse employees. Research on workforce diversity, equity, and inclusion in healthcare further indicates that improvements across demographic and experiential dimensions correlate with better patient safety outcomes, particularly in regions with diverse patient populations. Studies of corporate diversity statements show that companies emphasising identity-conscious topics receive more favourable employee evaluations of DEI, and a tri-balance framework for AI in personnel selection illustrates how efficiency, fairness, and stakeholder voice might be reconciled.

From these threads the review advances an integrative framework built on three interacting dimensions: the technological mechanisms of AI systems, organisational and governance mediators, and employee outcomes. The framework’s central claim is that empowerment outcomes are not determined solely by the technologies themselves but by the interaction between algorithmic design, organisational implementation practices, and governance structures. When transparency, fairness auditing, and inclusive organisational policies are implemented appropriately, AI systems may support more equitable and empowering workplace environments. When they are not, the same systems can entrench exclusion. This reframing positions AI not as a neutral technical artefact but as a sociotechnical governance issue shaped by organisational values and institutional structures, an account that aligns closely with emerging information systems scholarship on responsible AI and algorithmic governance. The author also notes, however, that the field’s empirical contributions remain fragmented and uneven, with equity research often disconnected from employee experiences and methodological innovations rarely integrated into discussions of ethics or well-being.

The review acknowledges its own limitations, including reliance on a single database, the imperfect reach of keyword-based searches, the possible influence of journal quality filters on corpus composition, and the inherent interpretive judgments of qualitative synthesis. Yet its research agenda is ambitious. The author calls for future work to connect social role and congruity theories with strategic human capital and corporate governance frameworks, to combine feminist design thinking with identity-consciousness debates, and to integrate neurodiversity and disability-led design perspectives with digital transformation research. On the empirical side, the review urges multi-country, longitudinal, and high-dimensional fixed-effects models to capture institutional, cultural, and regulatory heterogeneity, noting that labour laws, political climate, and social norms may shape how AI and DEI initiatives are implemented and received. With most current studies relying on cross-sectional or single-country designs, the consequences, rather than merely the determinants, of AI-augmented diversity management remain largely unexplored. What is already clear, the review concludes, is a fundamental tension at the heart of workplace AI: whether the technology becomes an instrument of algorithmic exclusion or a genuine engine of empowerment will depend on choices, about design, oversight, and governance, that organisations are making right now.

Subject of Research: A systematic review of how artificial intelligence affects the empowerment, equity, and inclusion of vulnerable and minority employees in the workplace

Article Title: Artificial Intelligence and the Empowerment of Vulnerable and Minority Employees: A Systematic Review

Article References: Pokharel, P. R. (2026). Artificial Intelligence and the Empowerment of Vulnerable and Minority Employees: A Systematic Review. Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10823-2

Image Credits: AI Generated

DOI: 10.1007/s10796-026-10823-2

Keywords: artificial intelligence, vulnerable employees, minority employees, algorithmic bias, diversity equity and inclusion, systematic review, human resource management, algorithmic governance, employee empowerment, workplace transformation, responsible AI, information systems