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July 29, 2026Machine LearningOpen Access

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

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Authors

BKBianka KowalskaHKHalina Kwaśnicka

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Overview

Survey reviews mechanistic interpretability approaches in neural networks, suggesting a path for deeper AI understanding.

Key Points

  • The aim is to synthesize existing work on mechanistic interpretability (MI) in neural networks to promote understanding and transparency.
  • Conducted a survey on mechanistic interpretability techniques and approaches.
  • Proposed a unified taxonomy for categorizing MI methods and provided examples and pseudo-code.
  • Contextualized MI within the wider field of explainable artificial intelligence (XAI).
  • Established a structured reference for mechanistic interpretability, aiding new researchers.
  • Highlighted the evolution and significance of MI in enhancing AI transparency.
  • Identified key techniques in MI, demonstrating their potential for understanding neural network decisions.

Cite This Study

Kowalska et al. (2026) studied this question.

synapsesocial.com/papers/6a69a2fcc8da07d9defa7308https://doi.org/10.1007/s10994-026-07129-4
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