ArticleNPJ artificial intelligence2026
Large language models reflect the ideology of their creators.
Article in NPJ artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Educating minds with generative AI.Communications psychology · 2026Review
- Article
- Ideological bias in the production of research findings.Science advances · 2026Article
- Synthetic political actors: an exploration into emerging AI-driven candidates and parties.Open research Europe · 2026Article
- Source framing triggers systematic bias in large language models.Science advances · 2025Article
- Exploring past and future fluency of temporal landmarks under reduced agency.Scientific reports · 2025Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) already play an influential role in how humans access information. However, their behavior varies depending on their design, training, and use. We prompt a diverse panel of 19 popular LLMs to describe 3,991 prominent persons with political relevance, and then judge how positively they portray each person. When comparing these assessments, we find disparities in ideological positions between LLMs across different geopolitical regions (Arabic countries, China, Russia, and Western countries), and across different languages (the United Nations' six official languages). Moreover, among only models from the United States, we find significant normative differences related to progressive values. Among Chinese models, we characterize division between internationally- and domestically-focused models. Our results suggest that the ideological stance of an LLM reflects the worldview of its creators. This poses the risk of political instrumentalization and raises concerns around technological and regulatory efforts aiming to make LLMs ideologically 'unbiased'.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.