Theory of Machine: Lay Beliefs About Algorithmic Data Processing Drive Recommendation Acceptance
研究消费者对AI系统所用数据类型的朴素理解，发现三种数据类型的心理建构通过感知个性威胁和处理可接受性影响推荐接受度。
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.