People are increasingly turning to AI assistance for simple tasks, e.g., arithmetic, spell-check, and answering simple questions. But does AI assistance actually save users time and effort? We investigate people's propensity to use AI for cognitively simple tasks and assess whether their reliance is well-calibrated. Across three pre-registered user studies (N = 2691), we find that people frequently choose to use AI even when doing so is inefficient (i.e. provides no meaningful time or effort savings). We identify systematic miscalibration at two levels: (1) a self-estimate miscalibration where people on average believe that they are using AI less than they actually are, and (2) efficiency-gain illusions where people overestimate how much time and effort savings AI use affords. We also identify a session-level carryover effect where a participant's prior AI use leads to further AI adoption and entrenches their miscalibration about time savings. Our results shed light on the mechanisms and biases underlying people's choice of whether to use AI as well as the risk of an overreliance feedback loop.

翻译：人们日益依赖人工智能辅助完成简单任务，例如算术运算、拼写检查和回答简单问题。但人工智能辅助是否真正为用户节省了时间和精力？本研究探讨了人们在认知简单任务中使用人工智能的倾向，并评估其依赖程度的合理性。通过三项预先注册的用户研究（N=2691），我们发现人们经常在低效（即无法实现有意义的时间或精力节省）的情况下选择使用人工智能。我们从两个层面识别出系统性的校准偏差：（1）自我估计偏差——人们平均而言认为自身使用人工智能的频率低于实际使用频率；（2）效率幻觉——人们高估了使用人工智能所能节省的时间和精力。我们还发现会话层面的遗留效应，即参与者之前使用人工智能的行为会导致其进一步采用人工智能，并加深其对时间节省的错误判断。这些结果揭示了人们选择是否使用人工智能的潜在机制与偏差，以及过度依赖反馈循环的风险。