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Meta-UX: Invisible Feedback Loops and Trust Friction in LLM Systems

Date: 2025-04-11

Summary

While providing multiple feedback reports on ChatGPT's UX and behavior, I noticed a recurring issue—not with the model’s output, but with how user feedback itself is handled. The current structure for user-submitted feedback creates an invisible loop: once the user submits, there is no acknowledgment, no ticket confirmation, no follow-up — not even a “Thank you” email. While this may be a conscious choice to reduce operational costs, from the user’s perspective, it creates ambiguity and erodes trust ...

Observed UX Friction

  1. No Confirmation Email or Ticket Reference

    • Users are often told that their feedback has been "forwarded," but no email arrives.
    • Unlike typical support systems (e.g., Zendesk, Intercom), there's no visible record of submission.

  2. No Feedback Lifecycle Visibility

    • Users don’t know if their report was useful, acknowledged, or led to any change.
    • Even in cases where improvements were clearly made later, there's no attribution or closure.

  3. Emotional Disconnect

    • This loop creates a psychological disconnect between user effort and platform responsiveness.
    • It leaves users feeling like their input may have vanished into a void — even if it actually helped.

Why This Matters

Feedback is labor — even more so when it’s well-structured, analytically grounded, and accompanied by concrete suggestions. While users like me may continue to offer insights voluntarily, the system's current opacity lowers the emotional ROI (return on investment) of continued contribution.

Suggestions for Improvement

  • Lightweight Acknowledgement

    • Even a simple in-chat message or UI signal saying “Thanks for your feedback” can help.
    • If follow-up isn’t possible, clearly state that in the interface to manage expectations.

  • Optional Feedback Tracker

    • Allow users to opt-in to receive update notifications if a report is used or referenced.

  • Clarify the Feedback Scope

    • Let users know what types of feedback are reviewed, and what might be archived silently.

Final Note

This is not a demand for more resources or personal recognition.
Rather, it's a suggestion to improve transparency, so users can feel that their input contributes to the system’s evolution — not just disappear into it.

Invisible processes cause visible distrust.
Sometimes, just knowing that someone is listening is enough.

[UPDATE] - Proposal for MVP Feedback Program

In addition to the points raised regarding feedback transparency and user trust, I would like to formally propose the introduction of an MVP Feedback Program for users who actively contribute detailed feedback and improvement suggestions.

MVP Feedback Program could:

  • Acknowledge and reward highly engaged users.
  • Create direct feedback channels for detailed user-driven system improvements.
  • Provide early access, visibility, and public recognition for MVPs.

This could help OpenAI create a stronger user base and refine the system even more effectively, while recognizing the time and effort put in by users to enhance the platform.

Why This is Beneficial:

  • Collecting high-quality feedback from dedicated users helps improve the system continuously.
  • Providing MVP status rewards voluntary contributions and provides recognition for feedback.
  • No additional cost involved in running this program since it's based on user self-engagement.
  • Mutual benefit: OpenAI gets valuable insights, and users feel valued without the need for formal employment or contracts.

By implementing an MVP Feedback Program, OpenAI would foster a win-win structure where users feel acknowledged for their contributions, driving further engagement and strengthening the community. It could also serve as an effective model for other companies to create similar voluntary feedback ecosystems.