Improving Human Deception Detection Using Algorithmic Feedback

Can algorithms help people detect deception in high-stakes strategic interactions? Participants watching the preplay communication of contestants in the TV show Golden Balls display a limited ability to predict contestants’ behavior, whereas algorithms do significantly better. To increase participants’ accuracy, we provide them with algorithmic advice by flagging videos for which an algorithm predicts a high likelihood of cooperation or defection. We test how the effectiveness of flags depends on their timing. We show that participants rely significantly more on flags shown before they watch the videos than flags shown after they watch them. These findings show that the timing of algorithmic feedback is key for its adoption. This paper was accepted by Marie-Claire Villeval, behavioral economics and decision analysis. Funding: Funding provided by an Innovation Grant for Inclusive Research Excellence at UC San Diego. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02792 .