发表机构
University of Oslo; BI Norwegian Business School（奥斯陆大学; BI挪威商学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究通过随机实验发现，大型语言模型的感知国籍不影响其说服力，用户虽对对手AI初始信任较低但仍会吸收其论点，国籍标签难抵御其带来的外国影响力。
AI 中文摘要
由地缘政治对手开发的对话式人工智能（AI）在全球范围内触达民众，引发了人们对其可能左右公众舆论或被作为外国宣传而遭拒绝的担忧，这对民主话语和信息主权产生了影响。然而，AI感知到的国籍是否会影响其说服力尚不清楚。在一项预先注册的随机实验中，来自美国全国代表性样本的403名成年人与一个聊天机器人进行了三轮辩论，该聊天机器人被介绍为美国的“DiscoveryAI”或中国的“ZhengheAI”，讨论的话题涉及政治或非政治。在所有条件下，参与者实际与同一个模型GPT-4o对话，该模型被要求反驳他们的初始立场。我们将对话前后的态度、信任和集体自恋的自我报告，与对1209轮参与者对话的计算分析相结合，包括大语言模型（LLM）编码的立场和论证行为、立场敏感嵌入，以及关键词掩盖的情感和毒性分类器。所有条件下的对话都产生了显著的态度改变。关键的是，国籍标签既不影响自我报告的态度改变，也不影响表达的立场、让步、反驳或情感，等效性检验和贝叶斯因子在很大程度上支持这些零效应。该标签唯一可靠的影响是，人们对中国模型的对话前类人信任较低，而功能信任未受影响。政治话题减缓了立场向AI立场的转变，集体自恋预测态度改变更少，无论国籍如何，其作为普遍障碍而非外群体过滤器发挥作用。因此，用户最初会对对手的AI保留社会信任，但仍会吸收其论点；国籍标签和透明度要求本身可能无法为抵御通过对话式AI开展的外国影响力行动提供有效保护。
英文摘要
Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as foreign propaganda, with consequences for democratic discourse and information sovereignty. Yet, whether an AI's perceived national origin shapes its persuasive power is unknown. In a preregistered randomized experiment, 403 adults from a nationally representative United States sample held a three-round debate with a chatbot introduced as either American ("DiscoveryAI") or Chinese ("ZhengheAI"), discussing a political or non-political topic. In all conditions, participants actually conversed with the same model (GPT-4o), instructed to argue against their initial position. We combined pre- and post-conversation self-reports of attitudes, trust, and collective narcissism with computational analyses of 1,209 participant turns, including LLM-coded stance and argumentative conduct, stance-sensitive embeddings, and keyword-masked emotion and toxicity classifiers. The conversations produced substantial attitude changes in every condition. Critically, the nationality label affected neither self-reported attitude change nor expressed stance, concessions, counterarguing, or affect, and equivalence tests and Bayes factors largely supported these null effects. The label's only reliable footprint was lower pre-conversation human-like trust in the Chinese model, whereas functionality trust was unaffected. Political topics slowed stance movement toward the AI's position, and collective narcissism predicted less attitude change regardless of origin, acting as a general barrier rather than an out-group filter. Users thus initially withhold social trust from a rival's AI yet still assimilate its arguments; origin labeling and transparency requirements alone may offer weak protection against foreign influence operations conducted through conversational AI.
