Artificial Intelligence in Human Resource Management: Exploring Public Policy Implications and Emerging Ethical Challenges

Abstract

This research examines the dual impact of Artificial Intelligence (AI) integration in Human Resource Management (HRM), focusing on the intersection of technological efficiency gains with emerging public policy requirements and ethical challenges. Employing a mixed- methods approach, the study combines quantitative survey data from 427 HR professionals across multiple sectors with qualitative policy analysis of regulatory frameworks from 12 jurisdictions and three in- depth organizational case studies. Structural Equation Modeling (SEM) was used to analyze relationships between AI adoption factors and ethical outcomes. AI adoption in HRM yields significant efficiency improvements (average 37.2% reduction in recruitment time, 31.8% cost reduction), but simultaneously introduces substantial ethical risks. Algorithmic bias was detected in 28.7% of systems, with gender bias being most prevalent (19.3%). Policy compliance gaps were substantial, with only 41.2% of organizations fully meeting GDPR requirements for AI systems. Organizations must develop comprehensive AI governance frameworks that balance efficiency gains with ethical safeguards. Policymakers should prioritize developing sector-specific AI regulations for HRM that address transparency requirements, bias auditing standards, and employee data protection. This study contributes a novel integrated framework for understanding the policy-ethics-technology nexus in AI-HRM adoption, providing empirical evidence of the specific trade-offs organizations face and offering actionable policy recommendations.