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.
How has ChatGPT adoption actually reshaped labor markets and information ecosystems? What drives sycophancy in language models and can it be fixed? How much do stated privacy concerns predict actual privacy-protective behavior?- 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?
- Do general populations show the same privacy awareness gaps as college-educated users?
- Why do people report equal privacy comfort despite removing vastly different amounts of personal information?
- Can editing interfaces create dark patterns where users add sensitive details back after abstractions?
- Can disclosure budgets protect users if they only account for factual data types?
- What privacy risks emerge when AI stores psychological rather than factual data?
- How stable are user privacy attitudes when major AI events occur?
- How do AI systems define 'private information' or 'high-stakes action' in training?