Adherence to Unethical Instructions from AI Supervisors: Combining Experiments with Machine Learning

Abstract

The role of artificial intelligence (AI) in organizations has fundamentally changed from performing routine tasks to supervising human employees. While prior studies focused on normative perceptions of AI supervisors, employees’ reactions towards them remained largely unexplored. We draw from theories on AI aversion and appreciation to tackle the ambiguity within this field and investigate if and why employees might adhere to unethical instructions either from a human or an AI supervisor. In addition, we identify employee characteristics affecting this relationship. To inform this debate, we conducted three experiments (N = 1,381) and used two state-of-the-art machine learning algorithms (causal forest and transformers). We consistently find that employees adhere less to unethical instructions from an AI than a human supervisor. Further, individual characteristics such as the tendency to comply without dissent or age constitute important boundary conditions. Additionally, Study 1 identifies that the perceived mind of the supervisors serves as an explanatory mechanism. (Pre-registered) Studies 2 and 3 further strengthen this notion. Our research generates insight into the ‘black box’ of human behavior toward AI supervisors and highlights organizational researchers can use machine learning methods as powerful tools to complement experimental research.