Improving Human Deception Detection Using Algorithmic Feedback

Marta Serra-Garcia and Uri Gneezy

No 10518, CESifo Working Paper Series from CESifo

Abstract: 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.

Keywords: detecting lies; machine learning; cooperation; experiment (search for similar items in EconPapers)
JEL-codes: C72 C91 D83 D91 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ain, nep-cbe, nep-cmp and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_10518

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