Leveraging Machine Learning for Advanced Human Resource Information Processing: Opportunities, Challenges, and Ethical Implications

Machine Learning (ML) has become a transformative tool in Human Resource Management (HRM), offering innovative approaches to enhance recruitment, performance evaluation, and workforce retention. This paper investigates the application of ML in HRM, focusing on how ML-driven models streamline recruitment processes by leveraging natural language processing to analyze resumes and predict candidate success. Additionally, it examines how data-driven insights from ML algorithms revolutionize performance evaluation, shifting from subjective assessments to continuous, metrics-based reviews. Furthermore, ML's predictive capabilities enable organizations to identify turnover risks and proactively address retention challenges. However, the integration of ML in HRM poses critical ethical challenges, including algorithmic biases, transparency issues, and employee privacy concerns. This study emphasizes the need for fairness-aware algorithms, continuous auditing, and transparent communication to build trust and accountability in ML-based HR systems. By addressing these concerns, the paper advocates for a balanced approach that leverages ML’s potential while ensuring ethical and sustainable practices in HRM.

Keywords:

Machine Learning (ML) , Human Resource Management (HRM) , Data-driven Insights , Ethical Challenges , Predictive Analytics