A Framework to Assess the Persuasion Risks Large Language Model Chatbots Pose to Democratic Societies

Abstract

We investigate whether large language models (LLMs) threaten democracy through their persuasive capabilities. Using two survey experiments (N = 10,417) and real-world simulations, we compare the cost-effectiveness of LLM chatbots against traditional campaign tactics, taking into account both the “receive” and “accept” steps in the persuasion process. Our design advances prior research by assessing extended human-LLM interactions and measuring short- and long-term effects across three political domains. We find that while LLMs are comparably persuasive to campaign ads once seen, real-world impact depends on both message reception and acceptance. Simulations estimate LLM-based persuasion costs $48–$75 per voter versus $100 for traditional methods. However, traditional methods currently scale more effectively. While LLMs do not yet offer substantially greater potential for large-scale persuasion, this may shift as capabilities improve and techniques for scalable exposure become feasible.