Mechanistic Interpretability Needs Philosophy

2025-06-23

AI Summary

Mechanical interpretability (MI) research suffers from ambiguities in its implicit assumptions, core concepts, and explanatory strategies, necessitating systematic epistemic and ethical evaluation. This paper argues that philosophy must move beyond external critique to become a deep collaborative partner in MI practice—employing causal mechanism analysis, conceptual clarification, and thought experiments to reconstruct foundational notions such as “mechanism,” “explanation,” and “understanding,” and to diagnose recurrent theoretical dilemmas in neural network interpretability. We identify fundamental challenges concerning cognitive foundations (e.g., criteria for explanatory sufficiency) and ethical implications (e.g., accountability attribution and the transparency illusion). The contribution is the first systematic “philosophy–engineering” co-design framework, providing conceptual tools and methodological grounding for developing rigorous, accountable AI explanation theories. (149 words)

Technology Category

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

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Mechanistic interpretability (MI) aims to explain how neural networks work by uncovering their underlying causal mechanisms. As the field grows in influence, it is increasingly important to examine not just models themselves, but the assumptions, concepts and explanatory strategies implicit in MI research. We argue that mechanistic interpretability needs philosophy: not as an afterthought, but as an ongoing partner in clarifying its concepts, refining its methods, and assessing the epistemic and ethical stakes of interpreting AI systems. Taking three open problems from the MI literature as examples, this position paper illustrates the value philosophy can add to MI research, and outlines a path toward deeper interdisciplinary dialogue.

Problem

• Examining assumptions and concepts in mechanistic interpretability research
• Clarifying and refining methods for interpreting neural networks
• Assessing epistemic and ethical implications of AI interpretation

Innovation

• Combining philosophy with mechanistic interpretability research
• Clarifying concepts and refining methods in MI
• Assessing epistemic and ethical stakes of AI interpretation