The Governance of Intimacy: A Preliminary Policy Analysis of Romantic AI Platforms

TL;DR

This paper analyzes the data governance practices of romantic AI platforms, revealing how they treat intimate disclosures as reusable data assets, raising privacy and ethical concerns.

Contribution

It provides a preliminary policy analysis highlighting default training practices, ownership issues, and risk shifts in romantic AI platforms' data governance.

Findings

Intimate disclosures are often treated as reusable data assets.

Platforms have broad permissions for data storage and analysis.

Key governance challenges include risk shifting and data ownership issues.

Abstract

Romantic AI platforms invite intimate emotional disclosure, yet their data governance practices remain underexamined. This preliminary study analyses the Privacy Policies and Terms of Service of six Western and Chinese romantic AI platforms. We find that intimate disclosures are often positioned as reusable data assets, with broad permissions for storage, analysis, and model training. We identify default training appropriation, ownership reconstruction, and intimate history assetization as key mechanisms structuring these practices, expanding platforms' rights while shifting risk onto users. Our findings surface key governance challenges in romantic AI and are intended to provoke discussion and inform future empirical and design research on human AI intimacy and its governance.