Abstract
It is typically assumed that for the successful use of machine learning algorithms, these algorithms should have higher accuracy than a human expert. Moreover, if the average accuracy of ML algorithms is lower than that of a human expert, such algorithms should not be considered and are counter-productive. However, this is not always true. We provide strong statistical evidence that even if a human expert is more accurate than a machine, interacting with such a machine is beneficial when communication with the machine is non-public. The existence of a conflict between the user and ML model and the private nature of user-AI communication will make the user think about their decision and hence increase overall accuracy.
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Goldberg, S., Salnikov, L., Kaiser, N., Srivastava, T., Pinsky, E. (2024). Correcting User Decisions Based on Incorrect Machine Learning Decisions. In: Arai, K. (eds) Advances in Information and Communication. FICC 2024. Lecture Notes in Networks and Systems, vol 921. Springer, Cham. https://doi.org/10.1007/978-3-031-54053-0_2
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DOI: https://doi.org/10.1007/978-3-031-54053-0_2
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