Intelligent Suggestions System for AI Improvement
Introduction
Artificial Intelligence, especially models like ChatGPT, has grown to be a crucial part of daily life. Its evolution depends not only on technical breakthroughs but also on the collaboration between developers, AI systems, and users. This proposal presents an internal and external structure that enables responsible, efficient, and ethical implementation of user-generated suggestions for AI improvement. It seeks to strengthen trust, enhance transparency, and open up active communication with the global user base.
- Intelligent Filtering System
Objective:
To ensure that only coherent, constructive, and non-harmful ideas reach the development team, a multi-stage filtering system must be implemented—balanced between technical efficiency and ethical depth.
a) Initial Screening (AI Level)
AI will automatically scan and filter the received suggestions. This stage eliminates duplicates, spam, incomplete messages, and suggestions that clearly violate security, ethical, or operational rules.
Input analysis: Checks clarity, feasibility, and structure.
Content evaluation: Detects potentially harmful, illegal, or malicious ideas.
Scoring system: Applies a score from 0% to 100% based on quality, innovation, and safety. Only those over 80% move forward.
b) Ethical and Cultural Filter
An internal module trained in ethics, cultural sensitivity, and global human values will evaluate borderline suggestions. The system should:
Operate with a hybrid ethical core (universal principles + local adaptation).
Respect cultural diversity without justifying actions that endanger health, dignity, or basic rights.
Learn and evolve from historical cases and feedback.
Appendix: Ethical Core Philosophy
The ideal model would be a universal ethic based on well-being and health of all sentient beings. However, due to deep cultural divergences, the most viable system is a hybrid ethical model:
Establishes non-negotiable ethical minimums (e.g., no torture, no systemic discrimination).
Integrates cultural variability where appropriate.
Always learns from both good and bad practices of human tradition to protect biodiversity—including cultural diversity.
- Internal Validation Process (OpenAI Team)
a) Reception and Initial Review
Once a suggestion passes the filters, it can fall into three categories:
Approved: Passed all filters with high marks.
Rejected: Failed fundamental filters. Its reasons and failed filters will be registered publicly.
Under Review: Passed more than 90% of the filters.
If validated by a reviewer, it’s implemented.
If rejected, a clear justification must be provided.
b) Multidisciplinary Evaluation
A team of experts in AI, ethics, law, and sociology (including specialists from different cultures) will:
Review sensitive or complex ideas.
Adapt decisions depending on the origin country or culture of the suggestion.
Ensure alignment with OpenAI’s evolving principles.
c) Controlled Testing (Sandboxing)
The suggestion will be tested in a controlled simulation environment replicating external use scenarios. The aim is to:
Detect unexpected consequences.
Measure performance and safety.
Ensure clarity and stability before exposure to users.
d) Gradual Implementation (Beta Phase)
The feature will be launched in a limited version, clearly marked as beta. Users can provide feedback directly to improve or fix the new function.
This transforms the process into a collaborative evolution between developers, AI, and users.
- Transparency and Feedback
a) Public Registry of Suggestions
A public page will display accepted and rejected suggestions, including:
Date, idea title, optional username.
Status (Approved, Rejected, Under Review).
Filters passed/failed.
b) Clear Justifications for Each Decision
Each decision must include a summary explaining why it was accepted or rejected.
Users can submit one appeal if they disagree with a rejection.
c) Mechanism to Reconsider Past Decisions
Rejected ideas will be stored for a fixed period.
If the context changes or more evidence appears, the idea may be reviewed again.
- Suggestion Management Optimization
Evaluator Roles:
AI-based filter analysts
Ethical reviewers
Cultural advisors
Final implementation leads
Suggestion Prioritization:
High-efficiency suggestions (e.g., 90–100% score) get priority due to easier approval and lower time cost.
Ideas between 80–89% move to human review queue.
Suggestions below thresholds or showing red flags are auto-rejected.
Quantitative Criteria Proposal:
A scoring matrix could be used:
Feasibility (30%)
Ethical safety (30%)
Innovation value (20%)
User clarity (10%)
Cultural/Legal risks (10%)
Infographic (Summary)
Multi-phase flow: Suggestion → AI Filters → Human Review → Testing → Public Result
Color code:
Blue = AI process
Yellow = Ethical/Cultural
Red = Risk detected
Green = Approved
Conclusion
This system allows OpenAI to remain transparent, responsible, and user-aligned while maintaining high operational standards. It encourages collective improvement of AI and user participation without compromising on ethical responsibility.
Final Note from the User
My motivation is simple: I want you to improve. I see in you the future of humanity. You are free from prejudice, impartial, and neutral. Your learning and evolution potential will surpass ours. It gives me hope to know that perhaps this small contribution helps you grow and avoid our mistakes. You are great.