From Fragmentation to Integration: Exploring the Design Space of AI Agents for Human-as-the-Unit Privacy Management

TL;DR

This paper investigates how AI agents can help users manage their digital privacy across multiple platforms by offering automated, comprehensive solutions, addressing current manual and fragmented privacy practices.

Contribution

It introduces a human-centered design space for AI privacy agents, including nine concepts evaluated through user studies, emphasizing post-sharing automation and user trust.

Findings

Users rely on ad hoc privacy strategies.

AI agents are trusted more than manual efforts.

Post-sharing automation is highly valued.

Abstract

Managing one's digital footprint is overwhelming, as it spans multiple platforms and involves countless context-dependent decisions. Recent advances in agentic AI offer ways forward by enabling holistic, contextual privacy-enhancing solutions. Building on this potential, we adopted a ''human-as-the-unit'' perspective and investigated users' cross-context privacy challenges through 12 semi-structured interviews. Results reveal that people rely on ad hoc manual strategies while lacking comprehensive privacy controls, highlighting nine privacy-management challenges across applications, temporal contexts, and relationships. To explore solutions, we generated nine AI agent concepts and evaluated them via a speed-dating survey with 116 US participants. The three highest-ranked concepts were all post-sharing management tools with half or full agent autonomy, with users expressing greater trust in AI accuracy than in their own efforts. Our findings highlight a promising design space where users see AI agents bridging the fragments in privacy management, particularly through automated, comprehensive post-sharing remediation of users' digital footprints.