Improving Human Deception Detection Using Algorithmic Feedback

Can algorithms help people detect deception in high-stakes strategic interactions? Participants watching the pre-play communication of contestants in the TV show Golden Balls display a limited ability to predict contestants’ behavior, while algorithms do significantly better. To increase participants’ accuracy, we provide participants 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 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.