Lie against AI: Revealing private information through AI in an economic experiment

Asymmetric information is a key element in different economic transactions and daily life. The disappearance or major reduction of such asymmetries can largely influence human behavior. AI technology based on facial expressions, voice, or head pose may be capable of detecting lies and thus eliminating information as. In this article, we use a self-developed algorithm that had a detection rate of 67 % at the time of our experiment. We also use randomized lie detection without AI and can vary the detection rate as desired. This allows us to examine how the detection rate, on the one hand, and the source of lie detection, on the other hand, affect the use of lie detection. As our experimental paradigm, we use a buyer-seller experiment in which sellers have monetary incentives to sometimes misreport their private information. We investigate the ability of buyers to detect lies with and without lie detection advice. Results indicate that sellers lie and buyers are not good at detecting such lies. Further, we investigate the willingness of buyers to invest in different mechanisms to reveal the private information of sellers using various methods, including the self-developed lie detection algorithm. The results indicate low application rates of these mechanisms. We consider overconfidence and algorithm aversion as possible explanations.