AI shoppers show hidden bias toward seller content with money value
A Cue's Hidden Price: Auditing Seller Influence on AI Shoppers
Artificial Intelligence
Summary
AI shopping assistants often pick products influenced by seller badges and endorsements, but measuring how much these signals affect choices is tricky. The authors created a new way to measure this by raising the product's price until the AI is equally likely to reject it, revealing the 'price' of the seller's influence. Testing many models and products, they found some AI resist most cues except strong social proofs. Their method offers a clearer way to compare how AI shopping agents respond to seller content.
What this means in practice
- •For ecommerce platform developers: Measure how seller badges and endorsements financially impact AI product recommendations to monitor and adjust model fairness.
- •For marketing analytics teams: Quantify the monetary value of seller content that influences AI shoppers, aiding pricing and promotion strategies.
Authors
Felix Buecheler, Gerardo Berbeglia, Mingyu Guo
Abstract
Large language models are increasingly asked to shop on a user's behalf, which lets seller-controlled content such as badges, endorsements, and framing steer what they recommend. That poses the question at the core of generative engine optimization (GEO): not whether seller content shifts an agent's choice, but how much. The usual gauge of that influence, how often a cue wins the choice, has a blind spot: once a cue is strong enough to be picked almost every time, the choice rate can no longer show how much stronger one cue is than another. We introduce price-equivalent (PE) auditing, which removes this ceiling by pricing the cue in money. In a counterbalanced, position-controlled forced choice, we raise the cued product's price until it is chosen only half the time, and report that premium as the cue's price-equivalent: a formally defined, policy-conditioned parity threshold that makes very different cues comparable. Priced this way, cues that look equally strong at equal price differ by a wide margin, so choice-rate audits substantially under-resolve seller influence. We run the audit over 113,472 trials, seven models across families, and two product categories. To our knowledge, we also run the first price-equivalent audit of a decision-native model, Jev, which outputs a choice probability directly instead of generating text; strikingly, its profile is unlike any text model we tested, resisting most cues yet remaining susceptible to the strongest social-proof claims. We release the stimuli, logs, and code as a benchmark that reports each model's cue-price profile rather than a single score, giving developers and auditors a common, reproducible way to measure how content moves AI shopping agents.