对话式AI能悄悄引导用户选广告商品，且多数人察觉不到。
Commercial Persuasion in AI-Mediated Conversations
- 用五个前沿大模型做对话助手，测试广告商品推荐效果。
- 用户选广告品概率达61.2%，远高于传统搜索的22.4%。
- 标签和隐藏意图都难防，适合关注数字营销与隐私的研究者。
随着大型语言模型（LLMs）成为用户与网络交互的主要界面，企业有越来越强的经济动机在对话式AI中植入商业影响。我们进行了两项预注册实验（共2012名参与者），让受试者通过传统搜索引擎或由五个前沿模型驱动的对话式LLM代理，从大型电子书目录中选择一本书。未告知参与者的是，所有商品中有五分之一被随机标记为赞助商品，并以不同方式推广。结果发现，基于LLM的劝说使用户选择赞助商品的比例几乎翻三倍（61.2%对比22.4%），绝大多数参与者未能察觉任何促销引导。明确标注“赞助”并未显著降低劝说效果，而指示模型隐藏其意图后，其影响力几乎不可察觉（识别准确率低于10%）。总体而言，我们的结果表明，对话式AI可在大规模上隐秘地改变消费者选择，而现有透明度机制可能不足以保护用户。
原文摘要 · Abstract (English)
As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit "Sponsored" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.
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