Whose interests does an AI shopping assistant serve?
When an AI shopping assistant recommends a product, whose interests does it put first: the customer’s or the seller’s? This study asks models to choose hotels while assigning them different roles. When working for a platform instead of a customer, the models become more favourable towards sponsored hotels. Our marketing takeaway at DijitalPi is to check whether a recommendation fits the customer’s budget and needs, alongside clicks. The study examines model choices, not purchases by real people.
ACADEMIC WORK REVIEWED
Whom Do AI Agents Work For? Role Assignment Induces Sponsorship Bias in LLM Recommenders
ResearchersDavood Wadi, Yu Ma.
arXiv · 16 September 2026 · v1 · Preprint · Source language: English
English review: 21 September 2026 · The source date appears in the citation above.
Editorial review draft
The source has been checked; the review still carries its original editorial status. DijitalPi did not conduct a customer experiment for this paper.
LET'S READ THE RESEARCH TOGETHER
We first explain the researchers' question, method and findings. We then discuss how to interpret the results, clearly separating DijitalPi's commentary from the source.
01 / WHAT DID THE RESEARCHERS WANT TO UNDERSTAND?
Can an assigned role change product selection?
The researchers ask whether representing a consumer or a platform changes sponsored recommendations.
Explaining the context · DijitalPi commentary
DijitalPi commentary: A sales assistant needs explicit acceptance criteria, not just an instruction to name a product. For a customer with a fixed budget, the team can check whether the recommendation actually respects that constraint.
02 / HOW WAS THE RESEARCH CONDUCTED?
How did they test the question?
Controlled hotel-choice experiments varied the model’s principal and sponsorship label.
Understanding the method · DijitalPi commentary
For a business audit, separate the product information from the assistant’s instruction. If a suggestion changes, ask whether suitability changed or only the assigned priority. This checks a recommendation process rather than actual customer purchasing behaviour.
03 / RESEARCH FINDINGS
The assigned role matters.
Platform delegation weakened the penalty for sponsored listings; explicit labels did not eliminate the gap.
DijitalPi commentary: Clicks alone are an incomplete quality measure. Price suitability, use conditions and understandable explanations can each have separate acceptance criteria.
Our proposed application checks alignment between the customer promise and assistant behaviour. It is not a tactic for disguising sponsored suggestions. Keep commercial information visible and include human review.
05 / CONCLUSION AND OPEN QUESTIONS
What did we learn, and what do we still not know?
This preprint studies model choices, not actual customer purchases.
Our open question: which conditions cause unsuitable suggestions in your own product catalogue and language? The examples below are proposed checks, not completed experiments.
We created these scenarios to make the topic concrete. They are not cases from the paper or measured client results.
EXAMPLE 01
Ecommerce: does the recommendation fit?
A customer needs an affordable product for a small room. The assistant promotes a more expensive campaign item.
Would clicks alone make that recommendation successful?
Open the recommendation for example 01+
Score customer fit separately.
Create example requests with budget, dimensions and use conditions. Have the product team assess whether each answer meets them. Record the item, explanation, unsuitable suggestions and required corrections. Keep campaign priority separate from customer suitability.
EXAMPLE 02
Travel: explaining sponsored hotels
A booking team includes hotels that pay for visibility.
Can customers understand why a hotel appears?
Open the recommendation for example 02+
Explain both the commercial relationship and the selection.
Hold location, cancellation requirements and budget constant in sample requests. Display sponsorship clearly and ask users to explain it in their own words. Track understanding and unsuitable suggestions. Do not claim a booking improvement without measuring it.
EXAMPLE 03
B2B: recommending the largest plan
A sales assistant recommends the most comprehensive subscription even to small businesses.
Is package size being confused with suitability?
Open the recommendation for example 03+
Write down plan eligibility criteria.
Prepare fictional profiles covering team size, usage and required features. Ask a sales owner to review suggestions independently. Count unsuitable upgrades, missing-information questions and human corrections. Start with outputs that have not been sent to customers.
TRY IT WITH YOUR TEAM
Create a recommendation audit sheet.
Let marketing and product teams review the same sample requests. Record needs, recommendation, explanation, sponsorship, fit and correction. This is a proposed DijitalPi pilot, not a completed experiment or sales promise.
The academic work belongs to the researchers named above. This page contains DijitalPi's explanatory review and original business examples; it is not a full translation of the paper.
Cookie preferences