Tracing Users' Privacy Concerns Across the Lifecycle of a Romantic AI Companion

2026-03-22

AI Summary

This study addresses the significant privacy and security risks posed by romantic AI companions during intimate interactions, particularly when users disclose highly sensitive information—a phenomenon lacking systematic understanding. Adopting a holistic lifecycle perspective encompassing access, disclosure, interpretation, retention, and exit, the research conducts content analysis and topic modeling on 2,909 Reddit posts spanning 79 subreddits. It identifies four key privacy challenge patterns: excessive access demands, heightened sensitivity within intimate contexts, interpretive ambiguity coupled with a sense of surveillance, and data irreversibility that imposes substantial burdens on user disengagement. These findings provide an empirical foundation and theoretical framework for governing privacy in affective AI systems.

Technology Category

• Philosophy and Ethics of AI: Privacy & Security
• Machine Learning: Privacy
• Humans and AI: Intelligent User Interfaces

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Romantic AI chatbots have quickly attracted users, but their emotional use raises concerns about privacy and safety. As people turn to these systems for intimacy, comfort, and emotionally significant interaction, they often disclose highly sensitive information. Yet the privacy implications of such disclosure remain poorly understood in platforms shaped by persistence, intimacy, and opaque data practices. In this paper, we examine public Reddit discussions about privacy in romantic AI chatbot ecosystems through a lifecycle lens. Analyzing 2,909 posts from 79 subreddits collected over one year, we identify four recurring patterns: disproportionate entry requirements, intensified sensitivity in intimate use, interpretive uncertainty and perceived surveillance, and irreversibility, persistence, and user burden. We show that privacy in romantic AI is best understood as an evolving socio-technical governance problem spanning access, disclosure, interpretation, retention, and exit. These findings highlight the need for privacy and safety governance in romantic AI that is staged across the lifecycle of use, supports meaningful reversibility, and accounts for the emotional vulnerability of intimate human-AI interaction.

Problem

• romantic AI
• privacy concerns
• intimate human-AI interaction
• data practices
• user vulnerability

Innovation

• lifecycle perspective
• romantic AI
• privacy governance
• emotional vulnerability
• meaningful reversibility