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

Bianka Kowalska, Halina Kwaśnicka

TL;DR

Mechanistic interpretability aims to reveal the internal algorithms of neural networks by mapping components to human-readable mechanisms. The paper defines MI, presents a unified taxonomy, and surveys techniques for feature localization, circuit discovery, and feature disentanglement, with concrete method-descriptions and examples. It discusses challenges like superposition, spurious correlations, and scalability, and outlines opportunities for debugging, privacy, robustness, and alignment. The authors argue that MI can enable a more scientific, trustworthy understanding of AI systems and advocate for broader adoption and methodological rigor.

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.

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

TL;DR

Mechanistic interpretability aims to reveal the internal algorithms of neural networks by mapping components to human-readable mechanisms. The paper defines MI, presents a unified taxonomy, and surveys techniques for feature localization, circuit discovery, and feature disentanglement, with concrete method-descriptions and examples. It discusses challenges like superposition, spurious correlations, and scalability, and outlines opportunities for debugging, privacy, robustness, and alignment. The authors argue that MI can enable a more scientific, trustworthy understanding of AI systems and advocate for broader adoption and methodological rigor.

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.

Paper Structure

This paper contains 13 sections, 1 equation, 7 figures, 2 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. What is Mechanistic Interpretability?
  3. Origins of Mechanistic Interpretability
  4. Defining Mechanistic Interpretability
  5. The Rise of Mechanistic Interpretability
  6. Current state of MI research
  7. Feature localization
  8. Circuit discovery
  9. Feature disentanglement
  10. Challenges and opportunities
  11. Challenges
  12. Opportunities
  13. Conclusions

Figures (7)

  • Figure 1: Brief taxonomy of XAI methods
  • Figure 2: Taxonomy of MI methods. The component and task criteria are not limited to the presented examples
  • Figure 3: Number of total publications and preprints referring to explainable, interpretable, and trustworthy AI since 2015. Data retrieved from Scopus (April 17th, 2025) by using the search terms: explainable artificial intelligence, interpretable artificial intelligence, trustworthy artificial intelligence
  • Figure 4: Number of total publications and preprints referring to mechanistic interpretability since 2015. Data retrieved from Scopus (April 17th, 2025) by using the search term: "mechanistic interpretability" and "mechanistic interpretability" AND "machine learning"
  • Figure 5: Illustration of probing. The probe does not interact with the neural network; rather it uses its representation to yield explanations. Note that probes can be applied at any layer of the network
  • ...and 2 more figures