Computational Processing of Political Bias Using AI Agents

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.