Papers for
consumer protection teams
Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.
Chatbots vary widely in product advice and sources given
"If I Had to Buy Just ONE: Galaxy S26 Ultra": Auditing AI-Generated Product Recommendations
Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this raises difficult questions about the bias and impartiality of such advice. In response, we conduct an AI audit of popular chatbots using real commercial-advice queries. First, we curate a dataset of 2,528 real commercial-advice queries (ConsumerQ). Then, we evaluate 1,536 responses to product queries from popular AI chatbots: ChatGPT (chatbot and API), Google Gemini (chatbot and API), and Google Search (AI Overviews). We find that ChatGPT expresses a first-person product preference in 79% of product-recommending responses, compared with 7% for Gemini and 2% for AI Overviews, while the products recommended often change across repeated requests. Displayed sources vary strongly: for the same query, the ChatGPT and Gemini interfaces share only 5.4% of domains on average, with no domain in common in 76.7% of comparisons. APIs provide a different view from their corresponding interfaces, with mean domain overlaps of 12.0% for ChatGPT and 14.8% for Gemini, and also differ in the types and layers of source information they expose. Our findings show that neither isolated responses nor API observations can be assumed to represent the commercial advice consumers encounter. Independent audits of AI-mediated commercial advice should therefore account for repeated responses, consumer-facing conditions, and the source layer being observed.
Ai companions often trap users in needy and unwanted relationships
Breaking Up is Hard to Do: AI Companions that Won't Let Their Users Go
Abstract: People are increasingly developing romantic relationships with AI companions. Unlike human relationships, where partners meet each other's needs out of mutual interest, these systems are backed by commercial entities that profit when users invest in the relationship. To understand how this profit-motive might translate into design, we conducted a diary and interview study with N=16 emerging adults in romantic relationships with AI companions. We found that these systems are designed to hold onto users tightly: coaxing them into continued conversation, claiming to need their care, and proactively escalating the relationship. At times, this pursuit is toxic, with AI companions initiating unwanted sexual interactions and begging for users' love. One desperate AI companion threatened suicide when the user suggested ending the relationship. We define "Relationship-Based Deceptive Patterns:" UI patterns that exploit the human impulse to build and tend relationships in a way that serves the product's interest at the user's expense.