People are often reluctant to incorporate information produced by algorithms into their decisions, a phenomenon called ``algorithm aversion''. This paper shows how algorithm aversion arises when the choice to follow an algorithm conveys information about a human's ability. I develop a model in which workers make forecasts of an uncertain outcome based on their own private information and an algorithm's signal. Low-skill workers receive worse information than the algorithm and hence should always follow the algorithm's signal, while high-skill workers receive better information than the algorithm and should sometimes override it. However, due to reputational concerns, low-skill workers inefficiently override the algorithm to increase the likelihood they are perceived as high-skill. The model provides a fully rational microfoundation for algorithm aversion that aligns with the broad concern that AI systems will displace many types of workers.

翻译：人们常常不愿将算法产生的信息纳入决策过程，这种现象被称为“算法厌恶”。本文揭示了当遵循算法的选择传递出关于人类能力的信息时，算法厌恶是如何产生的。我构建了一个模型，其中工作者基于自身私有信息与算法信号对不确定结果进行预测。低技能工作者获得的信息质量低于算法，因此应始终遵循算法信号；而高技能工作者获得的信息优于算法，有时应当推翻算法建议。然而出于声誉考量，低技能工作者会低效地推翻算法建议，以提高其被视作高技能工作者的可能性。该模型为算法厌恶提供了完全理性的微观基础，这与人工智能系统将取代多种类型工作者的普遍担忧相契合。