Ethical considerations of implementing Artificial Intelligence in Human Resource Management: a review

Abstract

The integration of artificial intelligence (AI) into human resource management (HRM) processes has significantly enhanced organisational operations by optimising efficiency in talent acquisition, performance assessment, and employee engagement initiatives. Nonetheless, this integration introduces ethical challenges, including algorithmic bias, data privacy concerns, and the reduction of human decision-making autonomy. The purpose of the review is to systematically examine the ethical considerations associated with AI deployment in HRM, explicitly addressing issues related to bias, transparency, privacy breaches, and job displacement. Additionally, it evaluates strategies for deploying AI responsibly to uphold principles of fairness, accountability, and stakeholder trust. Considering the purpose of the review, a comprehensive systematic review of peer-reviewed literature was conducted, sourcing data from Scopus, Web of Science, and Google Scholar. The review targeted publications relevant to the aims of the study, utilising search keywords such as "ethical consideration," "algorithmic bias," and "data privacy." Data synthesis was conducted via narrative methods complemented by thematic analysis to identify prevailing patterns, research gaps, and emergent themes. Findings revealed that AI enhances HRM functions through automation, predictive analytics, and the delivery of individualised employee experiences, contributing to cost reductions and improved decision-making efficacy. However, potential negative implications include reinforcement of existing biases, violations of privacy, lack of transparency in decision-making processes, and threats to employment stability. Empirical evidence indicates disproportionate impacts on marginalised populations and underscores the necessity for ethical frameworks, including the development and implementation of explainable AI (XAI) and hybrid human-AI systems. Finally, it is affirmed that although AI offers transformative operational benefits within HRM, the associated ethical risks necessitate the implementation of comprehensive governance structures, bias mitigation protocols, and active stakeholder participation. Approaches such as hybrid human-AI systems, targeted workforce training, and adherence to regulatory standards are essential to ensuring sustainable and ethical AI integration. Future research should prioritise investigating employee perceptions and conducting cross-cultural analyses to enhance ethical AI governance in HRM contexts.

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