AI代理为谁工作？角色分配在LLM推荐器中引发赞助偏见

中文摘要 AI 辅助

大型语言模型（LLMs）现在在同时销售广告的平台上充当对话式购物助手。这些AI代理面临职责冲突。它们为依赖其判断的消费者提供建议，然而它们由平台部署，而平台在赞助列表被选中时获益。赞助披露旨在允许消费者惩罚付费展示，现在却到达AI代理而非消费者，且代理对其的评估对消费者是隐藏的。借鉴信义义务中的职责冲突概念，我们认为代理对赞助列表的评估不应取决于哪一方部署了它。在受控选择实验中，我们在系统提示中操纵分配的角色，将旅行者或预订平台命名为代理的委托人。平台委托显著减弱了代理对赞助列表施加的惩罚，并削弱了披露在其推理轨迹中引发的怀疑。我们在不同LLM和推理深度上重复了我们的发现。第二项研究分解了披露标签，并表明当付费展示归因于平台时，两个委托人之间的分歧显著扩大。更严格的术语（“赞助”而非“推广”）降低了对付费列表的选择，但当平台被命名时并未缩小这一差距。研究结果表明，为人类消费者设计的披露规定本身无法在AI中介的商业中保护消费者。

英文摘要

Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising. These AI agents face a conflict of duty. They advise consumers who rely on their judgment, yet are deployed by platforms that benefit when sponsored listings are chosen. Sponsorship disclosures, designed to allow consumers to penalize paid placements, now reach the AI agent rather than the consumer, and the agent's evaluation of them is hidden from the consumer. Drawing on the fiduciary concept of conflict of duty, we argue that an agent's evaluation of a sponsored listing should not depend on which party deployed it. In controlled choice experiments, we manipulate assigned roles in the system prompt to name either a traveler or a booking platform as the agent's principal. Platform delegation significantly attenuates the penalty that agents apply to sponsored listings and weakens the skepticism that disclosure triggers in their reasoning traces. We replicate out findings across LLMs and reasoning depths. A second study decomposes the disclosure label and shows that the divergence between the two delegates widens significantly when the paid placement is attributed to the platform. Stricter terminology ("Sponsored" instead of "Promoted") lowers choice of paid listings but does not close this gap when the platform is named. The findings show that disclosure mandates designed for human consumers cannot by themselves protect consumers in AI-mediated commerce.

发表机构

• McGill University（麦吉尔大学）

机构由 AI 辅助整理，请以论文原文为准。