LLM assistants involving multiple people bring new risks and responses
When Single-User-Oriented LLM-based Assistants Involve Others: A Scoping Review of Pathways, Risks, and Responses
Human-Computer Interaction
Summary
Large language model (LLM) assistants are usually designed to help one person at a time, but they are increasingly used in situations with multiple people. The authors looked at 58 studies to understand how these assistants start including others, what risks come up, and how people try to manage them. They found five common ways others get involved, five main risk areas, and five ways people have responded to these risks. Their work suggests that assistants need better design and rules to handle interactions among multiple people while keeping each person's privacy and boundaries intact.
What this means in practice
- •For software designers: Design assistants that maintain clear personal boundaries when multiple users interact with the same system.
- •For policy makers: Develop governance frameworks addressing risks from assistants involving multiple people in real-world settings.
A survey. It maps existing work.
Authors
Yulin Chen, Yang Zhan, Zhuoran Lu, Qiao Jin
Abstract
LLM-based assistants are increasingly extending into multi-party contexts, while core operational processes for context management, personalization, identity attribution, authority attribution, and action execution often remain organized around a single user. Existing work examines particular multi-party settings, but lacks a systematic account of how these single-user-oriented assistants begin to involve additional human parties and what risks emerge. To address this gap, we conducted a scoping review of 58 studies. We identify five operational pathways spanning direct and indirect involvement, five recurring risk domains, and five areas of implemented and proposed responses. Based on these findings, we argue for governance that attends to changing cross-person roles and relationships in practice, and for assistant designs that preserve person-specific boundaries throughout interaction.