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

arXiv: https://arxiv.org/abs/2505.00036 · HTML: https://mlsanity.com/paper/2505.00036/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 74 references that are in the map.
- First posted on arXiv 29 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Model evaluation for extreme risks](https://mlsanity.com/paper/2305.15324/) (2023)
- [Generative Language Models and Automated Influence Operations: Emerging Threats and Potential Mitigations](https://mlsanity.com/paper/2301.04246/) (2023)
- [International AI Safety Report](https://mlsanity.com/paper/2501.17805/) (2025)
- [Labeling Messages as AI-Generated Does Not Reduce Their Persuasive Effects](https://mlsanity.com/paper/2504.09865/) (2025)

## Built on by (most cited first, 9 of 9)

- [The Levers of Political Persuasion with Conversational AI](https://mlsanity.com/paper/2507.13919/) (2025)
- [Beyond One-Way Influence: Bidirectional Opinion Dynamics in Multi-Turn Human-LLM Interactions](https://mlsanity.com/paper/2510.20039/) (2025)
- [AI systems out-persuade expert humans](https://mlsanity.com/paper/2606.16475/) (2026)
- [Persuasion Propagation: Does Persuasion Change AI Agent Behavior?](https://mlsanity.com/paper/2602.00851/) (2026)
- [Artificial intelligence can persuade people to take political actions](https://mlsanity.com/paper/2604.09200/) (2026)
- [The Hidden Costs of AI-Mediated Political Outreach: Persuasion and AI Penalties in the US and UK](https://mlsanity.com/paper/2603.27413/) (2026)
- [Beyond the Townhall: Spatial Anchoring and LLM Agents for Scalable Participatory Urban Planning](https://mlsanity.com/paper/2604.16348/) (2026)
- [Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study](https://mlsanity.com/paper/2608.11649/) (2026)
- [Belief Cascades Drive Persuasion in LLM Agent Networks](https://mlsanity.com/paper/2608.25152/) (2026)

## Abstract (arXiv)

In recent years, significant concern has emerged regarding the potential threat that Large Language Models (LLMs) pose to democratic societies through their persuasive capabilities. We expand upon existing research by conducting two survey experiments and a real-world simulation exercise to determine whether it is more cost effective to persuade a large number of voters using LLM chatbots compared to standard political campaign practice, taking into account both the "receive" and "accept" steps in the persuasion process (Zaller 1992). These experiments improve upon previous work by assessing extended interactions between humans and LLMs (instead of using single-shot interactions) and by assessing both short- and long-run persuasive effects (rather than simply asking users to rate the persuasiveness of LLM-produced content). In two survey experiments (N = 10,417) across three distinct political domains, we find that while LLMs are about as persuasive as actual campaign ads once voters are exposed to them, political persuasion in the real-world depends on both exposure to a persuasive message and its impact conditional on exposure. Through simulations based on real-world parameters, we estimate that LLM-based persuasion costs between 48-74 per persuaded voter compared to 100 for traditional campaign methods, when accounting for the costs of exposure. However, it is currently much easier to scale traditional campaign persuasion methods than LLM-based persuasion. While LLMs do not currently appear to have substantially greater potential for large-scale political persuasion than existing non-LLM methods, this may change as LLM capabilities continue to improve and it becomes easier to scalably encourage exposure to persuasive LLMs.