Poli-Bias: Understanding and Measuring Large Language Model Biases in International Political Conflicts

Abstract:Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, and legal reasoning that are difficult to capture with a single metric. In this work, we introduce Poli-Bias, a counterfactual framework for measuring whether LLMs treat legally equivalent conflict scenarios differently depending on the countries involved. Poli-Bias compares responses to paired prompts in which country identities are systematically swapped across diverse geopolitical relationships, legal violations, and reasoning tasks. Rather than reducing bias to a single judgment, our framework decomposes response disparities into five interpretable dimensions, revealing how and where unequal treatment manifests. Across 13 contemporary LLMs spanning diverse model families and sizes, we find that country identities and user affiliations can systematically affect how equivalent actions are described, evaluated, and defended under international law. Our results thus establish Poli-Bias as a fine-grained framework for auditing political even-handedness and sycophancy in LLMs.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

arXiv:2608.06123 [cs.AI]

(or arXiv:2608.06123v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.06123 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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