Correcting User Decisions Based on Incorrect Machine Learning Decisions

Saveli Goldberg, Lev Salnikov, Noor Kaiser, Tushar Srivastava, Eugene Pinsky

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.

External IDs:dblp:conf/ficc/GoldbergSKSP24