"Don't Look, But I Know You Do": Norms and Observer Effects in Shared LLM Accounts

TL;DR

This paper explores how users share LLM accounts, the social norms that develop, and how awareness of observation influences user behavior, with implications for designing multi-user AI platforms.

Contribution

It categorizes account sharing types, analyzes norm formation and fragility, and introduces design considerations for multi-user LLM platforms.

Findings

Four types of account sharing identified

Norm fragility linked to privacy concerns

Users adjust behavior due to observer effects

Abstract

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.