ExplainabilityTransparencyTrustworthinesscs.LG
Unboxing the Black Box: Mechanistic Interpretability for Algorithmic Understanding of Neural Networks
Bianka Kowalska, Halina Kwaśnicka
November 24, 2025
arXiv.org
7 citations
Key Takeaways
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Mechanistic interpretability aims to reverse-engineer how neural networks actually work by studying their inner computations and translating them into human-understandable algorithms, rather than just accepting them as black boxes that produce outputs.
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This approach is distinct from other explainability methods and represents a growing research area that treats AI models as systems worthy of scientific study and understanding, not merely as tools for solving tasks.
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Understanding neural network internals is increasingly critical for responsible AI deployment, as the paper emphasizes that the "black box nature of deep neural networks poses a significant challenge for the deployment of transparent and trustworthy AI systems" in society.
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The field has developed a unified taxonomy of mechanistic interpretability techniques, with concrete methods and examples that researchers can use to make AI decision-making more transparent and auditable.
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Mechanistic interpretability could support more scientific understanding of machine learning systems, potentially helping organizations and regulators better understand how and why AI systems make specific decisions—a key requirement for trustworthy AI governance.
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.