Rationale for RHF: A structured approach to channel AI's stochastic outputs into disciplined innovation, addressing the engagement-accuracy trade-off noted in The Conversation (2025).
4. The Refined Hallucination Framework (RHF)
Overview: A four-stage methodology (Generation, Filtering, Testing, Refinement) to transform AI hallucinations into actionable knowledge.
Stage 1: Generation
AI produces diverse, statistically plausible outputs, including hallucinations, using varied parameters (e.g., high-temperature settings).
Example: Generating speculative scenarios for AI ethics in 2050.
Stage 2: Filtering
Human experts select outputs based on novelty, plausibility, and alignment with domain goals.
Example: Choosing a hallucinatory economic model for further testing.
Stage 3: Testing
Empirical validation via simulations, experiments, or theoretical analysis.
