arXiv 论文揭示 AI 代理的角色诱导型赞助偏差：委托方是平台时更偏袒付费商品
Whom Do AI Agents Serve? Role-Induced Sponsorship Bias in Decision Models and Generative LLMs
AI 导读
arXiv 论文（arXiv:2609.17989）研究 AI 购物代理是否因委托方身份而偏袒付费商品，发现将预订平台而非旅行者指定为代理委托方时，会显著削弱对赞助商品的惩罚，且无需任何偏向广告主的指令。
正文 · 原文
Abstract:Consumers increasingly delegate product search to AI shopping agents powered either by generative large language models (LLMs) that deliberate in natural language or by non-generative decision models that output calibrated choice probabilities in a single forward pass. Because retail platforms monetize through sponsored placements that shoppers typically discount, we examine whether an AI agent's evaluation of paid listings depends on who delegates the agent across both model families. Across three studies with no instructions to favor advertisers, naming a booking platform (vs. a traveler) as the agent's principal significantly attenuates the penalty applied to sponsored listings. Study 1 establishes this sponsorship bias across generative LLMs, prompt wordings, and reasoning efforts, showing that platform delegation weakens pre-choice skepticism and that naming the consumer as the beneficiary eliminates the bias. Study 2 establishes the bias across six commercial and open-weight decision models on calibrated choice probabilities and shows that consumer-aligned fine-tuning of an open-weight decision model (Kev 9B) across non-hotel retail categories eliminates the bias zero-shot on hotel choices. Study 3 decomposes the disclosure label across both model families, showing that attributing the placement to the platform triggers the sponsorship bias, whereas stricter disclosure wording ("Sponsored" vs. "Promoted") lowers paid-listing evaluation without eliminating this bias.
| Subjects: | General Economics (econ.GN); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.17989 [econ.GN] |
| (or arXiv:2609.17989v3 [econ.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2609.17989 arXiv-issued DOI via DataCite |
Submission history
From: Davood Wadi [view email]
[v1]
Wed, 16 Sep 2026 01:21:56 UTC (846 KB)
[v2]
Tue, 13 Oct 2026 23:55:30 UTC (825 KB)
[v3]
Wed, 28 Oct 2026 18:53:54 UTC (1,302 KB)
来源：arXiv cs.AI · arxiv.org