Algorithmic Opacity, Organizational Justice, and Employee Behavioral Intentions: A Multilevel Socio-Technical Framework for Brazilian Organizations.

Abstract

The use of artificial intelligence in human resource management (AI-HRM) is expanding across recruitment, performance management, workforce analytics, and employee retention. Although AI-HRM may improve processing speed and standardization, opaque automated systems can make it difficult for employees to understand, question, or appeal employment-related decisions. This conceptual article develops a multilevel socio-technical framework for examining how algorithmic opacity may influence employee perceptions of procedural and informational justice, and subsequently shape algorithmic anxiety, workplace resistance, and turnover intention in Brazilian organizations. The framework distinguishes objective technical opacity, perceived algorithmic opacity, and system explainability. It integrates Socio-Technical Systems Theory, the Technology Acceptance Model, and Organizational Justice Theory while separating organization-level adoption and governance variables from employee-level perceptions and behavioral intentions. The framework also distinguishes legal requirements under Article 20 of Brazil’s Lei Geral de Proteção de Dados (LGPD) from voluntary governance practices and subjective employee evaluations of fairness. Six propositions are developed concerning opacity, explainability, justice, human-inthe-loop oversight, perceived system accuracy, and relational workplace preferences. A multilevel research design is proposed in which employees are nested within organizations, with organizational data collected from HR, technology, or compliance personnel and employee data collected through validated, culturally adapted instruments. The article makes no empirical claims; rather, it provides a theoretically bounded agenda for future research.