Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
econ.GN, cs.AI, q-fin.EC
Submitted: 2026-09-16
Updated: 2026-09-16
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
The gist: Large language models (LLMs) now serve as conversational shopping assistants on platforms that also sell advertising.
Terminology
Abstract
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.
Sources
- The Curious Case of Neural Text Degeneration
- Does Rank Still Matter? Position Bias When AI Agents Shop on Our Behalf
- Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases
- Self-Consistency Improves Chain of Thought Reasoning in Language Models
- Emergent Abilities of Large Language Models
Related papers
- How optimistic inflow forecasts distort dispatch, prices, and contracts in hydro-dominated power systems: evidence from Brazil
- The Economics of AI Inference: Inflation Dynamics, Welfare Costs, and Optimal Monetary Policy under the Inference-Cost Phillips Curve
- AI Economist Agent: An Agentic Framework for Evidence-Based Economic and Financial Analysis with RAG, Knowledge Graphs, and Large Language Models
- The Economics of Model Collapse: Equilibrium, Welfare, and Optimal Provenance Subsidies in Synthetic Data Markets
- Is Decentralized Finance Actually Decentralized? An Interdisciplinary Framework Integrating Network Theory, Agent-Based Simulation, and Longitudinal Evidence from Aave, GHO Issuance, and Cross-Chain Expansion
- Dynamic Resource Allocation with Karma: An Experimental Study