Feature Proposal: Self-Improving AI with On-Demand Transparency
I propose that AI models, like ChatGPT, be designed to quietly refine their factual accuracy over time based on both structured training data and verified user-provided corrections. However, rather than actively notifying users of these updates, the AI should operate silently in the background, only providing justifications and source evaluations when directly questioned about a response.
Key Benefits:
- Accurate Self-Correction:
- AI can verify and incorporate factual updates dynamically, ensuring that it continuously improves its knowledge base.
- Fact-checking mechanisms should cross-reference user input with multiple trusted sources to prevent misinformation.
- Misinformation Defense:
- AI should detect and reject attempts to manipulate facts through repeated false corrections.
- A trust-ranking system could prioritize verified sources and historically accurate corrections over unvetted user input.
- On-Demand Transparency:
- Users should be able to query the AI on any fact, prompting it to explain its reasoning, sources, and the history of any factual updates.
- This prevents blind trust while maintaining an efficient, evolving knowledge system.
- User-Centric Learning Without Overload:
- No constant “learning updates”—the AI improves without unnecessary notifications.
- When a user challenges a response, the AI provides a clear, sourced rationale rather than a vague or arbitrary correction.
This approach strikes a balance between adaptive AI learning and reliability, ensuring that AI continuously enhances its accuracy while maintaining user trust.