Should I Trust You? Detecting Deception in Negotiations using Counterfactual RL

TL;DR

This paper presents a novel AI approach using counterfactual reinforcement learning to detect deception in negotiations, specifically in strategic communication within the game Diplomacy, enhancing trust and decision-making.

Contribution

It introduces a method combining logical form extraction, value function analysis, and text features for improved deception detection in strategic negotiations.

Findings

High precision in detecting human deception

Outperforms Large Language Model baseline

Potential for AI-assisted trust verification tools

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 \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.