Correcting User Decisions Based on Incorrect Machine Learning Decisions
Abstract
. It is typically assumed that for the successful use of machine learning algorithms, these algorithms should have a 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 shows that even if a human expert is more accurate than a machine, an interaction 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 have the effect of making the user think about their decision and hence increase overall accuracy.
Cite
@article{arxiv.2411.10474,
title = {Correcting User Decisions Based on Incorrect Machine Learning Decisions},
author = {Saveli Goldberg and Lev Salnikov and Noor Kaiser and Tushar Srivastava and Eugene Pinsky},
journal= {arXiv preprint arXiv:2411.10474},
year = {2024}
}