Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild
TL;DR
JudgeGPT and RogueGPT are presented, a dual-axis framework that decouples ''authenticity'' from ''attribution'' to investigate the mechanisms of human susceptibility and suggest that ''pre-bunking'' interventions should target cognitive source monitoring rather than demographic segmentation to ensure trustworthy information ecosystems.
Abstract
As foundation models (FMs) approach human-level fluency, distinguishing synthetic from organic content has become a key challenge for Trustworthy Web Intelligence. This paper presents JudgeGPT and RogueGPT, a dual-axis framework that decouples ''authenticity'' from ''attribution'' to investigate the mechanisms of human susceptibility. Analyzing 918 evaluations across five FMs (including GPT-4 and Llama-2), we employ Structural Causal Models (SCMs) as a principal framework for formulating testable causal hypotheses about detection accuracy. Contrary to partisan narratives, we find that political orientation shows a negligible association with detection performance (r=-0.10). Instead, ''fake news familiarity'' emerges as a candidate mediator (r=0.35), suggesting that exposure may function as adversarial training for human discriminators. We identify a ''fluency trap'' where GPT-4 outputs (HumanMachineScore: 0.20) bypass Source Monitoring mechanisms, rendering them indistinguishable from human text. These findings suggest that ''pre-bunking'' interventions should target cognitive source monitoring rather than demographic segmentation to ensure trustworthy information ecosystems.
