ARCHIVES

Artificial intelligence (AI) is increasingly transforming human resource management through applications such as automated recruitment, candidate screening, workforce analytics, employee evaluation, and decision support. While these technologies can improve operational efficiency and support data-driven HR practices, they also introduce concerns related to algorithmic bias, transparency, privacy, accountability, and regulatory compliance. This case study examines the governance challenges associated with AI-enabled HR decision-making in an organizational context. A qualitative case study approach was adopted using organizational practices, governance processes, relevant documents, and available secondary evidence. The analysis focuses on AI ethics, algorithmic risk, human oversight, data governance, and regulatory compliance. The findings indicate that effective AI adoption in HR requires governance mechanisms that extend beyond technical performance and integrate ethical principles with systematic risk assessment and accountability structures. Key challenges include limited transparency in automated decisions, potential bias in data-driven models, privacy risks, and difficulties in maintaining compliance across different regulatory environments. The study highlights the importance of continuous AI auditing, meaningful human oversight, and clearly defined organizational responsibilities. The findings offer practical guidance for organizations seeking to develop responsible and sustainable AI governance frameworks for HR decision-making.