AI-DRIVEN PERFORMANCE APPRAISAL SYSTEMS: A CRITICAL LITERATURE REVIEW OF EMERGING ISSUES AND CHALLENGES

Abstract [English]

In the last decade especially, AI has been integrated into conceptual areas of human resource management, including performance appraisal and, through it, new efficiencies, objectivities, and accuracies have been achieved. But implementing AI-based performance management systems is not without its problems. Thus, in the following paper, a critical literature analysis of the emergent issues and risks relating to AI use in performance appraisal systems will be discussed. These are concerns such as the promotional and risk of bias inherent in algorithms, the opaqueness of processes involved, concern for privacy violation, and challenges relating to employee adoption. Among the pros of AI pointed out in the review, enhanced objectivity and efficiency of performance evaluations can be noted, besides numerous cons, with such severe drawbacks being bias and ethical questions which leave the questions of fairness and trust in AI as severe as ever. However, problems associated with understanding the AI models, technical framework, and educational preparedness of employees have posed difficulties in implementation of the AI across organizations. This paper also examines the limitations of the extant literature and highlights the need for strong governance architecture, ethically sound AI and change management efforts that can remedy these issues. The current literature review of this paper aims to give a precise approach of the research by identifying the challenges in AI-driven performance appraisals and make suggestions for future studies and integration of the HRM systems.