Interpretability as Alignment: Making Internal Understanding a Design Principle
This addresses the need for reliable governance in AI to bridge technical reliability and institutional accountability, though it is incremental in reframing existing interpretability approaches.
The paper tackles the problem of verifying internal alignment in frontier AI systems by proposing mechanistic interpretability as a technical substrate for private governance mechanisms, framing it as a design constraint to embed auditability and transparency within model architectures.
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.