Details

Autor(en) / Beteiligte

• Lanz, Lukas
• Briker, Roman
• Gerpott, Fabiola H.

Titel

Employees Adhere More to Unethical Instructions from Human Than AI Supervisors: Complementing Experimental Evidence with Machine Learning

Ist Teil von

• Journal of business ethics, 2024, Vol.189 (3), p.625-646

Ort / Verlag

Dordrecht: Springer Netherlands

Erscheinungsjahr

2024

Link zum Volltext

Quelle

PAIS Index

Beschreibungen/Notizen

• 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 such AI supervisors , employees’ behavioral 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 four experiments (total N  = 1701) 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. In addition, Study 1 identified that the perceived mind of the supervisors serves as an explanatory mechanism. We generate further insights on this mediator via experimental manipulations in two pre-registered studies by manipulating mind between two AI (Study 2) and two human supervisors (Study 3). In (pre-registered) Study 4, we replicate the resistance to unethical instructions from AI supervisors in an incentivized experimental setting. Our research generates insights into the ‘black box’ of human behavior toward AI supervisors, particularly in the moral domain, and showcases how organizational researchers can use machine learning methods as powerful tools to complement experimental research for the generation of more fine-grained insights.