Sensemaking and AI: Unraveling individuals' reactions to the black box in a three-study investigation
通过三项实验，研究了人们面对意外AI建议时为何会盲目接受或努力解释，发现自身框架的意外失败会促使“问题化转向”，导致忽略其他线索，可能损害决策绩效。
Artificial intelligence (AI) technologies promise to transform how people perform tasks and make decisions within organizations. Yet, their impact on human reasoning processes remains poorly understood. When encountering an unexpected AI suggestion, individuals may either attempt to understand the reasoning behind it or blindly accept or reject it. What drives these different reactions, however, remains unexplored. Unpacking these factors is essential to advance our understanding of augmentation and prevent major decision-making failures. This study addresses this gap through three experimental studies. In study 1 we find that, when performing a task, the unexpected failure of one's own frames increases the likelihood of individuals blindly accepting AI suggestions and effortfully trying to explain them. In study 2 we shed light on the underlying reasons for the results, by analyzing qualitative insights. We find that the unexpected failure of frames promotes “problematization pivoting”, a phenomenon wherein individuals anchor their reasoning to opaque AI suggestions ignoring other available cues. In study 3, we add evidence of potential negative performance implications associated with effects documented before. Overall, these findings contribute to the literature on human-AI augmentation and sensemaking theory, while also alerting managers and policymakers on the perils associated with AI use. • Frames failures foster the blind acceptance of unexpected AI suggestions. • Augmentation may lead to slower and inaccurate reasoning processes. • Sensemaking processes can be derailed by AI due to “problematization pivoting.” • Perceived uncertainty guides the different reactions to AI suggestions.