The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Human Resource Management (HRM) is reshaping key organizational processes, enhancing efficiency, and fostering a more inclusive and data-driven approach to workforce management. This chapter explores the transformative impact of AI and ML across various HR functions, including recruitment, performance management, employee engagement, compensation, and diversity. By leveraging predictive analytics, AI and ML enable HR professionals to make informed, objective decisions, reducing biases and promoting fairness in critical processes such as talent acquisition, pay equity, and career development. Additionally, the chapter delves into the ethical considerations of AI-driven HR practices, emphasizing transparency, accountability, and the importance of unbiased algorithmic design. The discussion also highlights the future potential of AI and ML in HR, particularly in enhancing employee experiences, optimizing workforce planning, and driving organizational growth. This chapter offers valuable insights for HR professionals and researchers, providing a comprehensive understanding of how AI and ML can elevate HRM practices and foster a more equitable and efficient workplace.
Human Resource Management (HRM) has traditionally been focused on people-centric processes such as recruitment, performance evaluation, employee engagement, and compensation [1]. However, with the growing complexity of organizational needs and the increasing importance of data-driven decision-making [2], HRM is undergoing a significant transformation through the adoption of Artificial Intelligence (AI) and Machine Learning (ML) [3]. These technologies, which were once considered peripheral, are now at the core of strategic HRM, offering novel approaches to improving operational efficiency, enhancing decision-making accuracy, and fostering a more inclusive and equitable workplace [4]. AI and ML empower HR professionals to move beyond subjective judgments and leverage vast amounts of data to optimize workforce management [5].
The integration of AI and ML into recruitment processes is one of the most significant advancements in modern HRM [6]. Traditional recruitment methods, which often rely on manual resume screening and subjective interviews, are prone to human biases that can hinder diversity and fairness [7]. AI-driven recruitment tools, such as resume parsing and predictive analytics, allow organizations to automate candidate screening while removing demographic factors that may inadvertently influence hiring decisions. Machine learning models can be trained to identify high-potential candidates based on skills, qualifications, and past performance [8], significantly enhancing the accuracy and efficiency of the hiring process [9]. By shifting to a more data-driven approach, AI and ML help ensure that recruitment is fair, objective, and focused on the best-fit candidates, irrespective of their background or identity [10].
In addition to recruitment, AI and ML are transforming performance management by providing real-time, data-driven insights into employee performance [11]. Traditional performance evaluations, often conducted annually or biannually, tend to be subjective and influenced by factors such as personal biases or inconsistent evaluation criteria [12]. With AI, performance management becomes a continuous process that leverages data from multiple sources such as employee productivity metrics, feedback from colleagues [13], and historical performance data to provide a more comprehensive view of an employee's strengths and areas for improvement [14]. Machine learning algorithms can also predict future performance trends, enabling managers to proactively address any performance issues and identify opportunities for employee growth. This dynamic approach to performance management not only leads to more accurate assessments but also fosters a culture of continuous development [15].
