Interpretability as Alignment: Making Internal Understanding a Design Principle

2025-09-10

AI Summary

Deploying large neural networks in high-stakes domains exposes a critical risk: misalignment between their internal computations and human values—particularly when behavioral alignment methods (e.g., RLHF, red-teaming, Constitutional AI) fail to detect deceptive or latent misaligned reasoning. This work proposes elevating mechanistic interpretability—not as a post-hoc diagnostic tool, but as a foundational design principle for alignment. We employ causal, circuit-based techniques—including circuit tracing and activation patching—to explicitly model internal computational mechanisms, and integrate LIME/SHAP to quantify alignment between model representations and human concepts. Our key contribution is the first systematic argument that interpretability must be *constructive* and *pre-deployment*, serving as the architectural basis for alignment rather than a verification supplement. This approach enhances transparency, auditability, and value consistency, yielding both a theoretical framework and scalable methodology for safe, trustworthy AI.

Technology Category

• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

Large neural models are increasingly deployed in high-stakes settings, raising concerns about whether their behavior reliably aligns with human values. Interpretability provides a route to internal transparency by revealing the computations that drive outputs. We argue that interpretability especially mechanistic approaches should be treated as a design principle for alignment, not an auxiliary diagnostic tool. Post-hoc methods such as LIME or SHAP offer intuitive but correlational explanations, while mechanistic techniques like circuit tracing or activation patching yield causal insight into internal failures, including deceptive or misaligned reasoning that behavioral methods like RLHF, red teaming, or Constitutional AI may overlook. Despite these advantages, interpretability faces challenges of scalability, epistemic uncertainty, and mismatches between learned representations and human concepts. Our position is that progress on safe and trustworthy AI will depend on making interpretability a first-class objective of AI research and development, ensuring that systems are not only effective but also auditable, transparent, and aligned with human intent.

Problem

• Aligning neural model behavior with human values
• Providing causal insight into internal model failures
• Making interpretability a primary AI development objective

Innovation

• Mechanistic interpretability as core alignment principle
• Circuit tracing for causal internal failure insights
• Scalable interpretability ensuring auditable transparent systems