TITLE:
Computational Processing of Political Bias Using AI Agents
AUTHORS:
Peter Theodoracopoulos, Manoj P. Vijayakumar, Vijay K. Madisetti
KEYWORDS:
Bias Mitigation, Iterative Refinement, Large Language Models, LLM as a Judge, Multi-Agent Systems, Political Bias, Political Compass, Social Media
JOURNAL NAME:
Journal of Information Security,
Vol.17 No.4,
October
28,
2026
ABSTRACT: Given the widespread presence of media bias favoring political groups on social media, discerning inherent bias is challenging. Existing methodologies often rely on an oversimplified, unidimensional (left/right) political spectrum. Furthermore, most research has focused on bias detection rather than mitigation. This paper introduces and evaluates POLQUAD, a novel multi-agent AI framework designed to mitigate political bias in social networks. Our approach utilizes the two-dimensional political compass (Authoritarian/Libertarian and Left/Right) to achieve nuanced classification. The POLQUAD framework employs specialized Opinion Agents, each prompted to represent one quadrant, to analyze text. A central Judge Agent synthesizes these perspectives and iteratively refines content toward neutrality. We conducted a study comparing our Full POLQUAD against two simpler iterative models (Unified and Naive) using samples from a dataset of 79,745 tweets from US Senators. All frameworks performed reasonably on text with low bias; however, the Full POLQUAD framework outperformed the others on a curated high bias dataset (bias magnitude > 7.0). On this dataset, the Full POLQUAD framework maintained high effectiveness while the Naive and Unified models’ performance collapsed. Importantly, the Full POLQUAD framework was the most efficient, converging on a neutral solution in fewer iterations. A subsequent ablation study confirmed that removing any of the four political agents degrades performance. The results validate that its synthesis-then-refine architecture provides a more stable, efficient, and robust solution for mitigating bias in highly polarized content.