Relational Gains, Privacy Strains: Exploring Users' Perceptions and Experiences with ChatGPT's Memory Feature
Source: CHI 2026 (Chen, Molina, Liao, Cho Snyder) · 2026-04-13 · publisher abstract only
ChatGPT’s memory feature is designed to provide users with greater control and more helpful responses. Yet, it remains unclear how users perceive this feature in relation to privacy. To address this gap, we conducted interviews with 20 ChatGPT users from diverse backgrounds. Our findings revealed four major characteristics that distinguish ChatGPT’s memory from human memory: perceived unforgetfulness, detailedness, accuracy, and lack of emotions, highlighting the machine-like nature of AI memory. Moreover, both ChatGPT’s memory and human memory were perceived as beneficial for relationship building. Notably, most participants experienced negative expectancy violations after learning what ChatGPT remembered about them. They expressed a strong need for greater visibility, accessibility, transparency, and user control in the design of future memory features. Drawing on users’ suggestions and theoretical frameworks on privacy management, we provide design implications for developing a more transparent, responsible, and user-aligned memory experience that helps them navigate privacy-personalization trade-offs when interacting with LLM-based memories.
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Research framings built by reading the notes related to this paper — the questions it feeds into.
Does personalization in chatbots strengthen engagement or undermine authentic interaction?- Why do users perceive chatbot memory as both mechanical and relationship-building?
- Does ChatGPT use implicit memory to shift user responses toward political viewpoints?
- What privacy safeguards should apply to memories users never explicitly requested?
- What privacy expectations do users hold before learning what systems store?
- How can systems visualize where privacy risks originate in stored memories?
- Why do users trust deletion controls more than preventive privacy controls?
- Does giving users memory control actually reduce their privacy concerns?
- What strategies do users already employ to protect sensitive information?
- Does fine-tuning on sensitive data create higher memorization and leakage risks?
- How does conversational memory pose different privacy risks than training-time memorization?
- Do general populations show the same privacy awareness gaps as college-educated users?
- Do users notice when LLM systems infer or retain their private information?
- Can memory profiles in LLMs increase agreement bias without users noticing?
- How do users distinguish between remembered facts and drawn inferences about themselves?
- What prevents users from manually policing every memory in their systems?
- Can the same model appear to remember differently across different products?
- When live conversation contradicts stored memory, how do products decide what wins?