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Interpretability as Alignment: Making Internal Understanding a Design Principle

Aadit Sengupta, Pratinav Seth, Vinay Kumar Sankarapu

TL;DR

Frontier AI systems demand verifiable internal alignment, not just surface-level behavior. The authors argue that mechanistic interpretability can serve as a design substrate by embedding auditability, provenance, and bounded transparency into architectures, linking causal abstraction theory with practical benchmarks like MIB and LoBOX. Their contributions include a conceptual bridge between mechanistic interpretability and private governance, a blueprint for interpretability-first architectures with audit hooks and provenance, and a roadmap for integrating existing benchmarks into governance workflows. This reframing positions interpretability as infrastructure for private AI governance that can bridge technical reliability and institutional accountability, enabling audits, certifications, insurance, and procurement standards.

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.

Interpretability as Alignment: Making Internal Understanding a Design Principle

TL;DR

Frontier AI systems demand verifiable internal alignment, not just surface-level behavior. The authors argue that mechanistic interpretability can serve as a design substrate by embedding auditability, provenance, and bounded transparency into architectures, linking causal abstraction theory with practical benchmarks like MIB and LoBOX. Their contributions include a conceptual bridge between mechanistic interpretability and private governance, a blueprint for interpretability-first architectures with audit hooks and provenance, and a roadmap for integrating existing benchmarks into governance workflows. This reframing positions interpretability as infrastructure for private AI governance that can bridge technical reliability and institutional accountability, enabling audits, certifications, insurance, and procurement standards.

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.

Paper Structure

This paper contains 8 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Introduction to Model Interpretability
  3. Mechanistic Interpretability: From Circuits to Alignment
  4. Building Governance-Ready AI: Interpretability-First Architecture
  5. Limitations and Critiques of Interpretability
  6. Comparing Alignment Approaches: Mechanistic vs. Behavioral Methods
  7. Conclusion
  8. Impact Statement

Figures (2)

  • Figure 1: A high-level conceptual map of mechanistic interpretability. It contrasts post-hoc approaches with mechanistic techniques, and illustrates core techniques, applications, limitations, and future directions.
  • Figure 2: Interpretability-Alignment-Governance Triangle: Bidirectional relationships between mechanistic design principles, regulatory audit capabilities, and governance frameworks. Each component constrains and enables the others, creating a feedback loop for interpretability-first AI development.