Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild

TL;DR

This study investigates human susceptibility to AI-generated misinformation using causal models, revealing that familiarity with fake news, not political bias, influences detection ability and highlighting a 'fluency trap' in AI outputs.

Contribution

Introduces JudgeGPT and RogueGPT frameworks and applies causal analysis to understand factors affecting human detection of AI hallucinations and disinformation.

Findings

Fake news familiarity correlates with detection performance (r=0.35).

Political orientation shows negligible impact (r=-0.10).

GPT-4 outputs bypass source monitoring, creating a 'fluency trap'.

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.