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

Data is an indispensable asset in the AI ecosystem. This paper 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 and four studies, we establish consumers’ mental construction of three different datatypes and experimentally validate two mechanisms that shape recommendation acceptance: perceived “individuality threat” associated with these datatypes and their “processing acceptability”.