Geopolitical alignment: Endorsement effects in large language models
2026-07-10 • Computers and Society
Computers and SocietyArtificial Intelligence
AI summaryⓘ
The authors tested whether large language models (LLMs) change how they rate international policies based on which country is said to support them. They found that some LLMs rated policies endorsed by the US or EU higher than the same policies endorsed by China or Russia. When asked to explain their ratings, the models often saw Western support as a sign of trustworthiness, while Chinese and Russian support raised concerns about risks like surveillance or data security. This suggests that LLMs’ evaluations are influenced by the country endorsing the policy, even if the policy itself doesn't change.
large language modelpolicy evaluationgeopolitical biasendorsement experimentinternational policyGPT-5Claude SonnetGeminiDeepSeekdata security
Authors
Maxim Chupilkin
Abstract
Large language models (LLMs) are increasingly used to summarize and evaluate policy-relevant information, but it remains unclear whether their judgments are implicitly shaped by geopolitical cues. I study this question with an endorsement experiment in which four LLMs evaluate the same international economic and security policies after each policy is randomly described as supported by the United States, the European Union, China, or Russia. In the numeric-only condition, GPT-5, Claude Sonnet, and Gemini rate China- and Russia-endorsed policies substantially lower than identical policies endorsed by the United States or the European Union; DeepSeek is the main exception. A second condition asks models to provide a short justification with the score. This request leaves the broad Western/non-Western gap intact for GPT-5 and Claude Sonnet, attenuates Gemini's penalties, and sharply activates China and Russia penalties in DeepSeek. The justifications indicate that Western endorsement is often treated as a credibility cue, whereas Chinese and Russian endorsement is treated as a cue for data security, sovereignty, surveillance, or geopolitical risk. These findings show that LLM policy evaluations can depend on the identity of a foreign endorser even when policy content is held fixed.