Aligning AI Through Internal Understanding
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This paper proposes mechanistic interpretability as a technical substrate for verifying internal AI alignment in frontier systems, arguing it enables governance mechanisms beyond behavioral compliance through causal evidence about model behavior.
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Abstract
Frontier AI systems require governance mechanisms that can verify internal alignment, not just behavioral compliance. Private governance mechanisms audits, certification, insurance, and procurement are emerging to complement public regulation, but they require technical substrates that generate verifiable causal evidence about model behavior. This paper argues that mechanistic interpretability provides this substrate. We frame interpretability not as post-hoc explanation but as a design constraint embedding auditability, provenance, and bounded transparency within model architectures. Integrating causal abstraction theory and empirical benchmarks such as MIB and LoBOX, we outline how interpretability-first models can underpin private assurance pipelines and role-calibrated transparency frameworks. This reframing situates interpretability as infrastructure for private AI governance bridging the gap between technical reliability and institutional accountability.
Summary
This paper argues that mechanistic interpretability is essential infrastructure for governing frontier AI systems through private governance mechanisms like audits, certification, and insurance. Rather than treating interpretability as post-hoc explanation, the authors propose embedding it as a design constraint within model architectures to generate verifiable causal evidence about model behavior. By integrating causal abstraction theory with empirical benchmarks (MIB and LoBOX), the paper outlines how interpretability-first models can support private assurance pipelines and role-calibrated transparency frameworks, bridging technical reliability with institutional accountability.
Cited by 3 pages
| Page | Type | Quality |
|---|---|---|
| Is Interpretability Sufficient for Safety? | Crux | 49.0 |
| Instrumental Convergence | Risk | 64.0 |
| Treacherous Turn | Risk | 67.0 |
eb734fcf5afd57ef | Stable ID: ZTg4NjIyZG