ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCE MANAGEMENT: RECRUITMENT, EVALUATION, AND EMPLOYEE RETENTION

Abstract

Artificial Intelligence (AI) is changing the future of Human Resource Management (HRM) in incomprehensible ways and ushering in fundamentally new business paradigms in HRM. It provided solutions and processes that would allow HRM to achieve better efficiencies, increase decision making and enhance the employee experience. This project explores AI applications in HRM by analyzing AI use in three traditional operational areas in HRM recruitment, evaluation, and retention while analyzing their contemporary fallacy of use, opportunities for AI based applications in HRM functions, and emergent ethical issues we must address now that AI technologies are being implemented in the workplace. With regard to recruitment, AI advancements have automated candidate sourcing, screen potential candidates employing Natural Language Processing and data analytics machine learning algorithms, as well as, preliminary interviewing. There are measurable benefits, including reduced time to hire and decreased operating costs. The promise is that using AI may advance objectivity by limiting or reducing human bias in hiring functions, more generally in the more entry level candidate sourcing, screening, and activities. However, the paper points out valid concerns related to the lack of algorithm transparency and fairness when using AI, and the potential for reinforcing systematic biases. The second area of focus for this study is AI in evaluation. The traditional performance appraisal approach of rate and evaluation is giving way to AI driven analytics engines that exist to collect information and data about behaviors, productivity, and goal achievement in real time. This breadth of analytic and extended assessment enable this team to offer employees timely feedback, immediate reinforcement to other employees for continuous learning and provide organizations with high potential talent manipulatives. However, the paper also addresses the psychological effect of AI on surveillance, trust, and morale in the workplace. The paper ultimately examines AI’s contribution to employee retention strategies. Predictive analytics models can review capability and performance information, attendance data, and employee sentiment data to identify systematic indicators of employee disengagement or potential turnover risk. HRM practitioners can use predictive analytics and predictive modelling to develop proactive retention interventions on behalf of the individual employee, and organizations are increasingly utilizing AI to develop customized training programs and career pathways which improve job satisfaction and retention. The paper surveys the literature, industry reports, case studies and HRM practitioners’ experiences and indicates that AI has the potential to generate efficiency gains and strategic benefits for HRM. HRM practitioners need to carefully consider the design and implementation of AI. Specifically, a human centered design approach where ethics, fairness, accountability, and regulatory requirements are considered is desirable to guarantee that AI being deployed as part of the workplace decision making process is responsible and equitable.