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 a random 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：人们往往不愿将算法生成的信息纳入自身决策，这一现象被称为"算法厌恶"。本文揭示了当选择遵循算法会传递关于人类能力的信号时，算法厌恶如何产生。我构建了一个模型：工人基于自身私有信息和算法信号对随机结果进行预测。低技能工人获取的信息质量劣于算法，因此应始终遵循算法信号；而高技能工人获取的信息优于算法，故有时可覆盖算法。然而，出于声誉考量，低技能工人会低效地覆盖算法以增加被视作高技能的可能性。该模型为与"人工智能系统将取代大量工种"这一广泛担忧相一致的算法厌恶现象提供了完全理性的微观基础。