ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Technology and Law
Volume 8 - 2025 | doi: 10.3389/frai.2025.1561322
Stakeholder-specific adoption of AI in HRM: Workers' representatives' perspective on concerns, requirements, and measures
Provisionally accepted
- University of Graz, Graz, Austria
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AI regulations aim to balance AI's potential and risks in general and human resource management (HRM) in particular. However, regulations are not finally defined and the perspectives of key stakeholders of HRM applications are not clear yet. Research on AI in HRM contributes only to a limited extent to the understanding of key HRM stakeholders, and the perspective of workers' representatives is especially lacking so far. This paper presents a study of three focus group workshops investigating workers' representatives' perspectives, to determine which concerns they perceive when using AI in HRM, which resulting requirements they have for adopting AI in HRM, and which measures they perceive as most suitable to fulfill them. Our results revealed that workers' representatives were critical of using AI across all HRM phases, particularly in personnel selection. We identified requirements and measures for adopting AI in HRM from the perspective of workers' representatives. These were summarized in a catalog including six dimensions: control, human oversight, responsibilities, transparency and explainability, lawful AI, and data security. Our findings shed a nuanced light on workers' representatives' needs, providing relevant insights for research on stakeholder-oriented adoption of AI in HRM and for specifying current AI regulations.
Keywords: artificial intelligence, Workers' representatives, requirements, Measures, HRM
Received: 15 Jan 2025; Accepted: 12 May 2025.
Copyright: © 2025 Malin, Fleiß and Thalmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Christine Malin, University of Graz, Graz, Austria
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