# Perceived Political Bias in LLMs Reduces Persuasive Abilities

arXiv: https://arxiv.org/abs/2602.18092 · HTML: https://mlsanity.com/paper/2602.18092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 52 references that are in the map.
- First posted on arXiv 20 Feb 2026.

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

- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Bias of AI-Generated Content: An Examination of News Produced by Large Language Models](https://mlsanity.com/paper/2309.09825/) (2023)
- [Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications](https://mlsanity.com/paper/2411.06837/) (2024)
- [The Levers of Political Persuasion with Conversational AI](https://mlsanity.com/paper/2507.13919/) (2025)

## Built on by (most cited first, 1 of 1)

- [The Hidden Costs of AI-Mediated Political Outreach: Persuasion and AI Penalties in the US and UK](https://mlsanity.com/paper/2603.27413/) (2026)

## Abstract (arXiv)

Conversational AI has been proposed as a scalable way to correct public misconceptions and spread misinformation. Yet its effectiveness may depend on perceptions of its political neutrality. As LLMs enter partisan conflict, elites increasingly portray them as ideologically aligned. We test whether these credibility attacks reduce LLM-based persuasion. In a preregistered U.S. survey experiment (N=2144), participants completed a three-round conversation with ChatGPT about a personally held economic policy misconception. Compared to a neutral control, a short message indicating that the LLM was biased against the respondent's party attenuated persuasion by 28%. Transcript analysis indicates that the warnings alter the interaction: respondents push back more and engage less receptively. These findings suggest that the persuasive impact of conversational AI is politically contingent, constrained by perceptions of partisan alignment.