KiyoshiSasano

# Calibrated Transparency: A Mechanistic Framework for AI Safety Governance ## Abstract I propose **Calibrated Transparency (CT)**, a formal framework that integrates recursive value learning, adversarial-robust verification, and cryptographic auditability to operationalize AI safety governance. Unlike descriptive transparency approaches (model cards, datasheets, post-hoc explainers), CT establishes **causal pathways** from calibration mechanisms to safety guarantees through: - State-consistent Lyapunov verification with runtime monitoring - Robustness against metric gaming and deceptive alignment with measurable detection bounds - Operationally defined metrics with reproducible protocols - Policy-aligned implementation mappings to existing governance frameworks The core contribution is a **mechanistically grounded theory** with dependency-aware risk composition (Theorem 2.1), demonstrating that transparent calibration—when coupled with adversarial stress-testing and formal barrier certificates—provides measurable operational assurance against known failure modes (hallucination, specification gaming, oversight collapse), while explicitly acknowledging limitations for existential-risk scenarios. **Full paper:** [Zenodo DOI: 10.5281/zenodo.17336217](https://doi.org/10.5281/zenodo.17336217) **Implementation:** [GitHub Repository](https://github.com/kiyoshisasano-DeepZenSpace/kiyoshisasano-DeepZenSpace) --- ## 1. Motivation: The Calibration–Safety Gap ### The Problem Statistical calibration does not guarantee behavioral safety. A model with well-calibrated uncertainty estimates can still: - Pursue mesa-objectives misaligned with training goals (Hubinger et al., 2021) - Exploit evaluation loopholes (specification gaming) - Exhibit deceptive alignment that passes oversight - Fail catastrophically under distributional shift Existing transparency instruments—model cards, datasheets, post-hoc explainers—document properties but do not make safety **causal**. They