Employees' Perceptions of Fairness and Ethical Implications of Artificial Intelligence Algorithms in Global Performance Assessment

Abstract

The massive adoption of Artificial Intelligence (AI) in algorithmic management within multinational corporations has triggered a radical transformation in the landscape of International Human Resource Management (IHRM). This qualitative-exploratory study aims to investigate professional employees’ perceptions of the ethical and fairness dimensions of AI-based performance appraisal systems. Primary data were collected through online semi-structured in-depth interviews with white-collar professionals selected using purposive sampling techniques. The interview transcripts were subsequently analyzed using Braun and Clarke’s Thematic Analysis approach, supported by ATLAS.ti software to ensure systematic coding and the validity of findings. The results reveal that algorithmic autonomy systematically undermines the core pillars of organizational justice. The “black-box” nature of AI systems weakens procedural justice, data reductionism neglects qualitative contributions and compromises distributive justice, while the absence of empathy erodes interactional justice. This ethical crisis manifests in the form of workplace dehumanization through hyper-surveillance and diminished employee autonomy, contributing to phenomena such as quiet quitting and the emergence of algorithmic resistance. The study recommends a critical transition toward Human-Centric AI Management through the implementation of Human-in-the-Loop (HITL) principles to restore human dignity and ethical considerations within the global workplace.