Submission candidate
False Governability and Runtime Governance
A technology-governance research line on how action-capable AI can appear governable while reconstructability collapses underneath.
Thesis
AI action scaling creates a review-scalability gap: organizations may preserve visible governance signals while losing the ability to reconstruct authority, evidence, review, and accountability.
Why it matters now
High-audit AI systems increasingly draft, route, classify, recommend, and act. Governance must therefore preserve action-episode accountability, not only policy language.
Evidence surface
- TFSC-facing manuscript rebuilt around false-governability onset under review scarcity.
- Taiwan 165 is used as a public-safe parameterization anchor for high-audit governance pressure.
- Evidence framing separates throughput from audit completeness, reconstructability, review backlog, and bounded-authority controls.
Validation path
- Governance mechanism
- Public-safe anchor
- Runtime accountability
- Submission proof
Current outputs
Manuscript package, conceptual figures, public-safe evidence architecture, and portal-proof artifacts.
Scope control
The website summarizes the governance mechanism and manuscript state. Protected records, legal claims, deployment claims, and domain-specific outcome claims require separate authorized evidence.
Questions
- How early can an organization forecast false-governability onset?
- Which traces preserve reconstructability when human review is scarce?
- What should governance block before unsupported action chains become institutional risk?