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Improving Human Deception Detection Using Algorithmic Feedback

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  • Marta Serra-Garcia
  • Uri Gneezy

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.

Suggested Citation

  • Marta Serra-Garcia & Uri Gneezy, 2023. "Improving Human Deception Detection Using Algorithmic Feedback," CESifo Working Paper Series 10518, CESifo.
    • Handle: RePEc:ces:ceswps:_10518
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    References listed on IDEAS

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    More about this item

    Keywords

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    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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