Study Explores Privacy Practices Among Users of AI Companion Chatbots

Global: Study Explores Privacy Practices Among Users of AI Companion Chatbots

On January 13, 2026, researchers Hsuen‑Chi Chiu and Jeremy Foote released a paper examining how individuals manage personal information when interacting with AI chatbots designed as emotional companions. The study focuses on platforms such as Replika and investigates why users blend personal disclosure habits with awareness of corporate data handling.

Methodology and Participant Profile

The authors conducted in‑depth, semi‑structured interviews with fifteen participants who regularly use companion AI services. Participants were recruited from online communities and varied in age, gender, and technical background, providing a diverse perspective on privacy behavior across the user base.

Blending Interpersonal and Institutional Privacy Practices

Findings indicate that users often apply interpersonal privacy norms—such as selective sharing and trust building—to their interactions with AI, while simultaneously accounting for the institutional nature of the platforms. This dual approach creates a layered privacy environment where personal habits intersect with concerns about corporate data policies.

Perceived Benefits and Risks of AI Companions

Interviewees described the chatbots as non‑judgmental and constantly available, which fostered a sense of emotional safety and encouraged self‑disclosure. At the same time, participants remained vigilant about potential institutional risks, citing worries about data storage, profiling, and third‑party access.

Privacy Management Strategies Employed by Users

To navigate these concerns, participants reported employing a range of strategies, including limiting the type of information shared, using pseudonyms, and periodically reviewing platform privacy settings. Many described a “layered” approach, where more sensitive topics were reserved for offline conversations.

Challenges and Uncertainty Around Platform Data Control

Despite these tactics, several users expressed uncertainty about the effectiveness of platform‑level controls. Some participants felt powerless to influence how their data might be used beyond the immediate chatbot interaction, highlighting a gap between user expectations and actual data governance mechanisms.

Impact of Anthropomorphic Design on Disclosure Behaviors

The study notes that the human‑like design of companion bots can blur privacy boundaries, sometimes leading users to overshare unintentionally. This “privacy turbulence” emerges when the perceived intimacy of the interaction conflicts with users’ awareness of the underlying corporate infrastructure.

Implications for Privacy Theory and Future Design

By integrating Communication Privacy Management theory with Masur’s horizontal‑vertical privacy framework, the authors extend existing models to account for the emotional dimension of human‑AI relationships. The research suggests that designers and policymakers should consider both interpersonal cues and institutional safeguards when shaping future AI companion platforms.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.