"Don't Look, But I Know You Do": Norms and Observer Effects in Shared LLM Accounts
This research addresses the problem of adapting single-user AI platforms to multi-user realities for users sharing accounts, highlighting incremental insights into social norms and privacy tensions.
The study investigated how large language model (LLM) subscriptions are shared among users, identifying four sharing types based on owner usage and cost-sharing, and found that users adjust their behavior due to privacy concerns and observer effects. It combined a survey of 245 users and interviews with 36 participants to analyze norm formation and fragility in this social practice.
Account sharing is common in subscription services and is now extending to generative AI platforms, which are still primarily designed for individual use. Sharing often requires workarounds that create new tensions. This study examines how LLM subscriptions are shared and the norms that develop. We combined a survey of 245 users with interviews of 36 participants to understand both patterns and lived experiences. Our analysis identified four types of account sharing, organized along two dimensions: whether the owner uses the account and whether subscription costs are shared. Within these types, we examined how norms were formed and how their fragility, especially privacy, became evident in practice. Users, fully aware of this, subtly adjusted their behavior, which we interpret through the lens of the observer effect. We frame LLM account sharing as a social practice of appropriation and outline design implications to adapt single-user platforms to multi-user realities.