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

Document Type : Original Article


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

2 Faculty member, Faculty of Entrepreneurship, Farshi Moghadam Street

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



The integration of Artificial Intelligence (AI) into various sectors has been a transformative force, revolutionizing traditional practices and introducing new efficiencies. However, this integration is not without its challenges. One sector that stands to benefit immensely from AI integration is Human Resources Management (HRM), a field that is central to the functioning of any organization. Yet, the incorporation of AI into HRM brings to the fore a significant challenge - that of model transparency. This challenge forms the crux of this study, which aims to elucidate the role of Explainable Artificial Intelligence (XAI) within HRM.

XAI, a subfield of AI, focuses on creating AI models that are not just efficient but also interpretable and explainable. The importance of XAI becomes particularly pronounced in sensitive areas like HRM, where decisions can have far-reaching impacts on individuals' careers and lives. Therefore, it is crucial for AI systems used in HRM to be not only technically efficient but also ethically sound and transparent. This study underscores this necessity and delves deeper into the role and potential of XAI in HRM.

The study conducts a scientometric analysis of research conducted over the past decade (2013-2023) in the field of XAI and its application in HRM. In this study, scientometric analysis serves as a tool to map the landscape of XAI research, identify key themes and patterns, and understand its intersection with HRM.

The analysis begins with an exploration of general XAI concepts. This exploration lays the groundwork for understanding the broader context of XAI, its principles, its techniques, and its potential applications. It provides a comprehensive overview of the field of XAI, highlighting its multidisciplinary nature and its vast potential for application in various sectors.

Following this, the study delves into the specific application of XAI in HRM. This is a relatively new area of exploration and one that holds significant potential for transforming HRM practices. By employing co-occurrence analysis, a method used to detect themes or patterns in a body of text, the study identifies key patterns and themes in XAI research and its intersection with HRM.

The co-occurrence analysis includes of two distinct knowledge maps. The first map focuses solely on XAI, providing a comprehensive overview of the field. It reveals that XAI applications are predominantly linked to the medical field, with less emphasis on human sciences. This finding suggests that while there is significant potential for XAI in HRM, this area has not yet been fully explored or exploited.

The second knowledge map examines the synergy between XAI and HRM. Interestingly, the integration of HR in AI revealed a lack of significant correlation with AI themes. This finding indicates a gap in research and innovation at the intersection of these two fields. This gap could stem from divergent backgrounds, perspectives, or a reluctance of HR managers to adopt intelligent systems.

The study also identifies technical machine learning and ontological explainability as core aspects of XAI application. These aspects underpin the ability of XAI to provide transparent and understandable AI models. However, the minimal interaction between technical AI and HR indicates the need for interdisciplinary research that combines HR expertise with AI to develop more relevant and effective HR tools.

In conclusion, this study provides a comprehensive overview of the current state of XAI and its application in HRM. It highlights the significant potential of this field, while also identifying key challenges and areas for future research. It is hoped that this work will contribute to the ongoing dialogue on the role of AI in HRM and inspire further exploration and innovation in this exciting field. The findings of this study underscore the necessity for AI systems, particularly in sensitive areas like HRM, to be not only technically efficient but also ethically sound and transparent. This is critical for ensuring that AI systems are used responsibly and that they contribute positively to HRM practices.


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