Demystifying Artificial Intelligence (AI) in Human Resource Management (HRM): A Bibliometric Analysis of Explainable Artificial Intelligence (XAI) (2013-2023)

Document Type : Original Article

Authors

1 Department of Business Creation entrepreneurship, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran

2 Entrepreneurship Development Group, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran

3 Department of entrepreneurship development, Faculty of Entrepreneurship, University of Tehran, Tehran, Iran

Abstract

Purpose: The integration of Artificial Intelligence (AI) has impacted various sectors, bringing about significant changes. However, these applications come with challenges. One sector that has notably benefited from AI is Human Resource Management (HRM). The adoption of AI in HRM
has raised concerns regarding transparency. This study explores the role of Explainable Artificial Intelligence (XAI) within HRM. The importance of XAI is especially critical in sensitive areas like HRM, where AI systems must be not only efficient but also ethically responsible and transparent. Building on this premise, the study further investigates the role and potential of XAI in HRM.
Method: This study conducts a scientometric analysis of research published over the past decade (2013–2023) in the field of Explainable Artificial Intelligence (XAI) and its applications in Human Resource Management (HRM). Scientometric (bibliometric) analysis is employed as a tool to map the XAI research landscape, identify key themes and patterns, and explore its intersection with HRM. The analysis begins by examining general XAI concepts, including its principles, techniques, and potential applications, before focusing on the specific application of XAI in HRM. Additionally, co-occurrence analysis is utilized to detect themes and patterns within the body of research.
Findings: The co-occurrence analysis in this study presents two distinct knowledge maps. The first map focuses exclusively on explainable artificial intelligence (XAI), offering an overview of
the field. It reveals that XAI applications are predominantly associated with the medical domain,
with comparatively less emphasis on the humanities. This finding suggests that, although XAI holds significant potential for human resource management (HRM), this area remains underexplored and underutilized. The second knowledge map investigates the intersection between XAI and HRM. Interestingly, the integration of HR within AI exhibits a notable lack of correlation with core AI topics. This indicates a research and innovation gap at the convergence of these fields, potentially arising from differing disciplinary backgrounds, perspectives, or HR managers' reluctance to adopt intelligent systems. Additionally, the study identifies explainable machine learning techniques and ontological frameworks as core elements of XAI applications, which support XAI’s capacity to deliver transparent and interpretable AI models. However, the limited interaction between technical AI and HR highlights the necessity for interdisciplinary research that combines HR expertise with AI to develop more relevant and effective HR tools.
Conclusion: In conclusion, this study provides an overview of the current state of explainable artificial intelligence (XAI) and its applications in human resource management (HRM). It highlights the significant potential of this field while identifying key challenges and areas for future research. The findings emphasize that AI systems, particularly in sensitive domains such as HRM, must be not only technically efficient but also ethically sound and transparent. This is essential to ensure that AI systems are used responsibly and contribute positively to HRM practices. It is hoped that this work will contribute to the ongoing dialogue about the role of AI in HRM and inspire further exploration and innovation in this dynamic field.

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Main Subjects

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Sciences and Techniques of Information Management
Volume 11, Issue 2
July 2025
Pages 93-132

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