政府はAI学習データを通じチャットボット応答を形成し得る（Governments May Shape What AI Chatbots Say by Shaping the Data They Learn From）

2026-05-13 ニューヨーク大学（NYU）

Photo credit: Andriy Onufriyenko/Getty Images

＜関連情報＞

• https://www.nyu.edu/about/news-publications/news/2026/may/governments-may-shape-what-ai-chatbots-say-by-shaping-the-data-t.html
• https://www.nature.com/articles/s41586-026-10506-7

国家メディア統制は大規模な言語モデルに影響を与える State media control influences large language models

Hannah Waight,Eddie Yang,Yin Yuan,Solomon Messing,Margaret E. Roberts,Brandon M. Stewart & Joshua A. Tucker
Nature  Published:13 May 2026
DOI:https://doi.org/10.1038/s41586-026-10506-7

Abstract

Millions of people around the world query large language models (LLMs) for information. Although several studies have compellingly documented the persuasive potential of these models^{1,2,3,4,5,6,7,8,9,10}, there is limited evidence of who or what influences the models themselves, leading to a flurry of concerns about which companies and governments build and regulate the models. Here we show through six studies that government control of the media across the world already influences the output of LLMs via their training data. We use a cross-national audit to show that LLMs exhibit a stronger pro-government valence in the languages of countries with lower media freedom than in those with higher media freedom. This result is correlational, so to triangulate the specific mechanism of how state media control can influence LLMs, we develop a multi-part case study on China’s media. We demonstrate that media scripted and curated by the Chinese state appears in LLM training datasets. To evaluate the plausible effect of this inclusion, we use an open-weight model to show that additional pretraining on Chinese state-coordinated media generates more positive answers to prompts about Chinese political institutions and leaders. We link this phenomenon to commercial models through two audit studies demonstrating that prompting models in Chinese generates more positive responses about China’s institutions and leaders than do the same queries in English. The combination of influence and persuasive potential across languages suggests the troubling conclusion that states and powerful institutions have increased strategic incentives to leverage media control in the hopes of shaping LLM output.