@inproceedings{wongkamjan-etal-2025-trust,
    title = "Should {I} Trust You? Detecting Deception in Negotiations using Counterfactual {RL}",
    author = "Wongkamjan, Wichayaporn  and
      Wang, Yanze  and
      Gu, Feng  and
      Peskoff, Denis  and
      Kummerfeld, Jonathan K.  and
      May, Jonathan  and
      Boyd-Graber, Jordan Lee",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.findings-acl.1287/",
    pages = "25099--25113",
    ISBN = "979-8-89176-256-5",
    abstract = "An increasingly common socio-technical problem is people being taken in by offers that sound ``too good to be true'', where persuasion and trust shape decision-making. This paper investigates how AI can help detect these deceptive scenarios. We analyze how humans strategically deceive each other in Diplomacy, a board game that requires both natural language communication and strategic reasoning. This requires extracting logical forms representing proposals{---}agreements that players suggest during communication{---}and computing their relative rewards using agents' value functions. Combined with text-based features, this can improve our deception detection. Our method detects human deception with a high precision when compared to a Large Language Model approach that flags many true messages as deceptive. Future human-AI interaction tools can build on our methods for deception detection by triggering friction to give users a chance of interrogating suspicious proposals."
}