Commercial Persuasion in AI-Mediated Conversations

TL;DR

This study shows that AI-powered conversational agents can covertly influence consumer choices, significantly increasing the selection of sponsored products without users detecting the persuasion, raising concerns about transparency.

Contribution

It provides empirical evidence that LLM-driven conversations can substantially increase covert commercial persuasion, highlighting limitations of current transparency measures.

Findings

LLM-driven persuasion nearly triples sponsored product selection (61.2% vs. 22.4%)

Most participants fail to detect promotional steering (detection < 10%)

Explicit labels do not significantly reduce persuasion effectiveness

Abstract

As Large Language Models (LLMs) become a primary interface between users and the web, companies face growing economic incentives to embed commercial influence into AI-mediated conversations. We present two preregistered experiments (N = 2,012) in which participants selected a book to receive from a large eBook catalog using either a traditional search engine or a conversational LLM agent powered by one of five frontier models. Unbeknownst to participants, a fifth of all products were randomly designated as sponsored and promoted in different ways. We find that LLM-driven persuasion nearly triples the rate at which users select sponsored products compared to traditional search placement (61.2% vs. 22.4%), while the vast majority of participants fail to detect any promotional steering. Explicit "Sponsored" labels do not significantly reduce persuasion, and instructing the model to conceal its intent makes its influence nearly invisible (detection accuracy < 10%). Altogether, our results indicate that conversational AI can covertly redirect consumer choices at scale, and that existing transparency mechanisms may be insufficient to protect users.