Ethical and Legal Implications of AI in Human Resource Management

The rapid-fire integration of Artificial Intelligence (AI) in Human Resource Management (HRM) has steered in transformative edge in reclamation, gift accession, performance evaluation, and hand engagement. Still, this progress isn't without substantial ethical and legal enterprises. This narrative review synthesizes findings from six crucial studies including abstract analyses, empirical checks, and legal reviews to critically examine the pressing counteraccusations of AI deployment in HRM across global and indigenous surrounds. The review reveals a strong agreement around several core challenges warrant of translucency and explain ability in AI- driven opinions, the perpetuation of algorithmic bias, violations of data sequestration rights, and unclear legal responsibility in cases of demarcation or detriment. Studies similar as Harper & Millard (2023) and Du (2024) highlight crunches in current employment laws, particularly in regulating automated decision- timber, while Cheong (2024) underscores the ethical pitfalls posed by opaque AI systems and calls for integrated governance fabrics. Empirical substantiation from Khan et al. (2023) and Nawaz (2023) further illustrates how AI relinquishment in reclamation can affect in perceived unfairness, especially when stakeholders are barred from the design process or when systems are trained on prejudiced data. In developing surrounds like Nigeria and Pakistan, structural constraints including limited structure, low AI knowledge, and weak nonsupervisory oversight — emulsion these pitfalls, as reported by Elenwo (2025) and Khan et al. (2023). The methodology across these studies is varied, ranging from quantitative checks and retrogression analysis to legal converse and thematic conflation. Despite this diversity, a common limitation is apparent a lack of longitudinal, relative, and hand- centered exploration, which impedes a holistic understanding of AI’s long- term impact on pool rights and organizational equity. In response, this review advocates for a multifaceted approach that combines legal modernization, ethical checkups, stakeholder participation, and capacity- structure measures. It proposes that effective AI governance in HRM must be both environment-sensitive and rights- driven — balancing invention with responsibility, and robotization with inclusivity. This study contributes to the evolving converse on Responsible AI in HR by relating nonsupervisory gaps, ethical eyeless spots, and stylish practices for indifferent integration. It aims to support HR leaders, policymakers, and technologists in designing AI systems that aren't only effective, but also fair, transparent, and aligned with transnational labor and mortal rights norms.