  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.
