Unboxing the Black Box: Mechanistic Interpretability for Algorithmic Understanding of Neural Networks

TL;DR

This paper reviews mechanistic interpretability (MI) as a method for understanding neural networks by reverse engineering their inner computations, emphasizing its role in advancing transparent AI systems.

Contribution

It provides a unified taxonomy of MI approaches, analyzes key techniques with examples, and contextualizes MI within the broader XAI landscape, highlighting its potential for scientific understanding.

Findings

MI offers a promising path for transparent AI

The paper categorizes and analyzes key MI techniques

MI can support scientific understanding of neural networks

Abstract

The black box nature of deep neural networks poses a significant challenge for the deployment of transparent and trustworthy artificial intelligence (AI) systems. With the growing presence of AI in society, it becomes increasingly important to develop methods that can explain and interpret the decisions made by these systems. To address this, mechanistic interpretability (MI) emerged as a promising and distinctive research program within the broader field of explainable artificial intelligence (XAI). MI is the process of studying the inner computations of neural networks and translating them into human-understandable algorithms. It encompasses reverse engineering techniques aimed at uncovering the computational algorithms implemented by neural networks. In this article, we propose a unified taxonomy of MI approaches and provide a detailed analysis of key techniques, illustrated with concrete examples and pseudo-code. We contextualize MI within the broader interpretability landscape, comparing its goals, methods, and insights to other strands of XAI. Additionally, we trace the development of MI as a research area, highlighting its conceptual roots and the accelerating pace of recent work. We argue that MI holds significant potential to support a more scientific understanding of machine learning systems -- treating models not only as tools for solving tasks, but also as systems to be studied and understood. We hope to invite new researchers into the field of mechanistic interpretability.