Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL
This addresses the socio-technical issue of people being misled by too-good-to-be-true offers, though it is incremental as it builds on existing methods for specific scenarios.
The paper tackles the problem of detecting deceptive offers in negotiations by analyzing human communication in the board game Diplomacy, achieving high precision in deception detection compared to a Large Language Model approach.
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 \abr{ai} can help detect these deceptive scenarios. We analyze how humans strategically deceive each other in \textit{Diplomacy}, a board game that requires both natural language communication and strategic reasoning. This requires extracting logical forms of proposed agreements in player communications and computing the relative rewards of the proposal 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-\abr{ai} interaction tools can build on our methods for deception detection by triggering \textit{friction} to give users a chance of interrogating suspicious proposals.