# 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)

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## 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 tell us what a model does, not why it's safe.

### The Approach

**Calibrated Transparency** formalizes transparency as a causal safety mechanism:

- Calibrated uncertainty gates actions (abstention when uncertain)
- Lyapunov certificates constrain policy updates (formal stability guarantees)
- Recursive preference checks deter reward hacking
- Cryptographic proofs make claims auditable (zk-STARKs for range predicates)

### Scope ...

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