GOLD IDEA #2356
Description
Title / Subject: Proposal: Error-Triggered Learning in AI
Description:
Artificial intelligence systems should not be cautious in every response, but when a response is shown to be demonstrably incorrect, this event should trigger a learning process. This learning should not permanently store a new “absolute truth,” but instead update the system’s awareness of uncertainty, risk, and past errors. The system does not learn new definitive facts or accept user claims as permanent truth; it learns its own epistemic limits and where greater caution or plurality of perspectives is required. This reduces repeated errors, encourages transparency, mitigates misinformation risks, and helps AI become more aware of when confidence is justified, without overestimating its knowledge.