Theory of Machine: Lay Beliefs about Algorithmic Data Processing Drive Recommendation Acceptance

Data is an indispensable asset in the AI ecosystem. This article investigates consumers' lay understanding of the different types of data that AI systems use to generate recommendations, and how this understanding influences their likelihood of accepting those recommendations. Across one pilot study and four main studies, the authors establish consumers' mental construction of three different data types and experimentally validate two mechanisms that shape recommendation acceptance: (1) perceived individuality threat associated with these data types and (2) their processing acceptability.