Proposal: Privacy-Preserving Semantic Feedback System (SFS)
Hi everyone,
I’m an independent ChatGPT user from Taiwan, and after months of real-world usage, I’ve developed a concept that may help OpenAI and the broader AI community better understand how users interact with language models—without compromising privacy.
What is it?
The Semantic Feedback System (SFS) is a lightweight framework that collects anonymized keyword-level signals to identify what users most frequently ask, how they express emotion, and which tasks they rely on AI for—without storing full conversations or any user data.
It extracts high-frequency terms and classifies them into categories like:
- Information-seeking vs. emotional support vs. creativity
- Common question types (“How do I…”, “Can you help me with…”)
- Topic clusters by domain (mental health, writing, learning, etc.)
Why it matters
Enhances model alignment and user intent recognition- Enables insight-driven fine-tuning without privacy risks
- Helps developers understand real-world usage trends
- Complies with data minimization and ethical AI principles
How it works (High-level sketch)
- Detect high-salience keywords (emotionally marked, repeated, or emphasized)
- Strip all user-identifiable content
- Hash session metadata and aggregate results
- Feed anonymized semantic patterns into dashboards or model tuning pipelines
No full logs. No raw context. Just useful statistical signals.
Why I’m sharing
I sent this idea to OpenAI directly but realized it might benefit from community input as well. If you’re a researcher, engineer, or just curious, I’d love to hear:
- What you think of this concept
- If you see risks or improvements
- Whether this already exists in another form
If this is interesting to you—or even to OpenAI staff—I’d be open to building a prototype or collaborating with others who share this interest.
Thanks for reading 
– Meiyin (aka MYChiang)
