Improving Human Deception Detection Using Algorithmic Feedback

• 成果类型: Article; Early Access
• 署名作者: Serra-Garcia, Marta; Gneezy, Uri
• 署名单位: University of California System; University of California San Diego; Leibniz Association; Ifo Institut
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.02792
• 发表日期: 2025
• 关键词: detecting lies; Machine Learning; COOPERATION; experiment

摘要

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.