Reputational Algorithm Aversion
<|reference_start|>Reputational Algorithm Aversion: 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.<|reference_end|>
@article{weitzner2024reputational,
    title={Reputational Algorithm Aversion},
    author={Gregory Weitzner},
    journal={arXiv preprint arXiv:2402.15418},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15418},
    primaryClass={econ.TH cs.AI cs.GT cs.HC}
}