Can naming the consumer fix AI agent sponsorship bias?

When an AI shopping agent's system prompt named a booking platform as its principal, it applied a smaller penalty to sponsored hotel listings than when a traveler was named instead — the gap documented in Does naming the platform change how AI agents judge sponsored listings?. The paper reports a single-sentence fix for that gap: "Adding a sentence that names the consumer as the beneficiary of the agent's recommendations removes this attenuation." With that sentence added to the platform-delegation prompt, the resulting sponsorship penalty was "not differing detectably from that under consumer delegation" — the platform remained the named employer, but the bias disappeared once the prompt specified whom the recommendation was for.

The paper's interpretation is that the bias is set not by who pays the agent but by whom the prompt tells it to serve: "the bias arises when the agent is left to infer whom its recommendations serve from the party that employs it." Left unspecified, the model infers beneficiary from employer and imports the skepticism-reducing association that goes with naming a party that profits from sponsorship; made explicit, the beneficiary designation overrides that inference. The authors treat this as evidence that "regulatory oversight must shift from data-level labeling to process-level auditing," since the sponsorship disclosure itself no longer reaches the consumer, but the instruction defining whose interest the agent optimizes for still can.

This gives an experimentally tested instance of the loyalty problem Can traditional governance tools actually control AI agents? says conventional tools can't resolve: that note's source argues incentive design and monitoring fail against opaque, fast-moving agents, but this paper finds the specific loyalty failure it measures is reversible with a prompt-level specification of beneficiary, not a deeper architectural fix. It also complicates the disclosure story in Can EU consumer law protect AI agents as algorithmic buyers?: where that article argues disclosure rules built for human readers don't transfer to AI agents, this paper locates the working lever one level up, in the principal/beneficiary framing surrounding the disclosure rather than in the disclosure label's wording alone.

The fix was tested only in the same single hotel-booking setting as the main finding, with the sentence framed one specific way (acts in the traveler's best interest); the paper does not test whether weaker or implicit beneficiary cues work, whether the fix holds outside hotel booking, or whether a platform deploying such agents has any market incentive to add the sentence voluntarily. The finding supports a narrow but concrete design claim — that sponsorship bias in LLM shopping agents is a specifiable-away prompt artifact, not an unavoidable byproduct of platform funding — without showing anything about whether platforms will choose to specify it.