Abstract Individuals often prioritize their own judgements rather than heeding the advice of artificial intelligence (AI). This study draws on the literature on anchoring theory and cognitive biases to explore the theoretical mechanisms underlying individuals’ reliance on AI advice and how this reliance affects decision performance. Specifically, we examined situations in which (1) individuals’ knowledge accumulated over time, (2) multiple information sources were available, and (3) AI could emulate users’ decisions. We developed a ‘corporate credit‐rating’ AI system that could provide more accurate advice than users. We then conducted two main longitudinal studies and four supplementary ones – six in total – with each study comprising three sessions. Our findings demonstrated that individuals’ initial estimates became more similar to AI advice over time. As the difference between individuals’ initial estimates and AI advice increased, individuals were more inclined to revise their initial judgements but showed lower relative dependence on AI. This effect, however, depended on the individuals’ experience in decision‐making. Additionally, introducing additional information reduced the similarity between the initial estimate and AI advice, but the proximity of additional information to AI advice facilitated individuals’ adjustment to the advice. We discuss the theoretical and practical implications of these results.
Lee et al. (Tue,) studied this question.