AI’s Impact on Talent Acquisition Strategies and Employee Engagement Methodologies: Ethical Considerations for Trustworthy AI-HRM Integration

Human Resource Management (HRM) is being transformed by Artificial Intelligence (AI), which automates fundamental areas like talent acquisition and workforce planning, together with employee engagement and performance management. AI technologies provide organizations with efficient operations and predictive insights that help refine hiring processes and employee satisfaction while optimizing workforce distribution. The use of AI in HRM brings about substantial ethical issues such as algorithmic bias, together with transparency deficits and data privacy risks, and a reduction in human oversight. AI systems that learn from past datasets may propagate discrimination throughout hiring procedures and performance assessments by strengthening current workplace prejudices. The implementation of AI surveillance tools for employee monitoring brings up fundamental questions about workplace privacy and ethical practices while challenging notions of consent. Organizations should implement fairness-aware AI models along with explainability frameworks and robust data governance policies while incorporating hybrid AI-human decision-making methods for proper AI integration. HRM applications of AI demand ongoing bias evaluations alongside adherence to data protection regulations and clear AI decision processes to uphold accountability and trustworthiness. Through an extensive review, this paper investigates how AI affects HRM operations while identifying ethical risks and proposing governance strategies to achieve an equilibrium between automation and ethical responsibility. Future investigations must prioritize creating regulatory structures along with enhancing AI bias reduction methods and analyzing how AI influences long-term workforce diversity and employee job conditions, and well-being. HRM departments that prioritize ethical AI governance will fully harness AI capabilities while maintaining decision-making processes that are transparent and fair to build trust within organizations.