Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems
This work addresses the adverse impacts of AI on information quality for digital ecosystems, presenting incremental practical tools for research.
The paper tackles the problem of AI-generated misinformation by developing JudgeGPT and RogueGPT as tools to study human perception and detection, finding that detection has improved but remains in competition with generation.
Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and multimodal systems. Central to this work are our practical contributions: JudgeGPT, a platform for evaluating human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine for research. Together, these tools form an experimental pipeline for studying how humans perceive and detect AI-generated misinformation. Our findings show that detection capabilities have improved, but the competition between generation and detection continues. We discuss mitigation strategies including LLM-based detection, inoculation approaches, and the dual-use nature of generative AI. This work contributes to research addressing the adverse impacts of AI on information quality.