Human Approval
AI-organized issues or proposals cannot proceed to the next audience judgment stage without human review and approval.
Risk Mitigated: Unreviewed exposure of AI framing
Trust by design
Trust stems not from AI confidence, but from safeguards.
Beyond declaring good intentions, it blocks incorrect role combinations and exaggerated claims from execution paths, leaving a record for review.
Multiple Independent Perspectives
AIs from different companies handle independent proposals, mutual reinforcement, modification proposals, and voting.
Risk Mitigated: Monopoly on generation, evaluation, and justification by a single model
Blocking False Precision
Express uncertain policy impacts as direction and range, not as a single precise number with authority.
Risk Prevented: Concealing uncertainty with the authority of numbers
Reproducibility
Record model, version, temperature, seed, prompt, rationale, and assumptions in the execution record.
Risk Prevented: Inability to explain the same result again
Honest Disclosure
Disclose non-reflected items, conflicts, costs, and constraints at the same level as reflected content.
Risk Prevented: Selective exposure of adopted proposal advantages only
Prevention of public opinion misreading
Distinguish virtual/actual, sample/non-sample, prediction/example narratives; place non-representativeness disclosure with results.
Risks prevented: Expanding experimental responses to national public opinion
Defense mechanism original text will be provided with RFC and executable code links after repository open transition. Currently, Please see the open-source release status.
Honest limits
Unresolved issues are also included in the main text.
Open source release is not a declaration of completion but a stage to enable external review.
Limitations of the methodology
- Outcomes depend on who sets the agenda and options.
- Virtual participants cannot predict real human behavior.
- Choice changes alone cannot prove deliberation quality or democratic legitimacy.
- AI-generated example narratives are not future predictions.
Current Implementation Limitations
- Guard code for event tools and domain-neutral core remains duplicated.
- AI cross-deliberation relies on external APIs and networks.
- Authentication, DB, and persistent work structures for online real participant operations are in preparation.
- First real audience on-site demonstration is scheduled for 2026-08-12.
Opposition and failure are not noise to remove but research
records to preserve.
The goal is not to increase consensus rates. Subsequent reviewers must see why agreement failed, what was not reflected, and which experiments failed.