Mechanistic interpretability
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Mechanistic interpretability (often abbreviated as mech interp, mechinterp, or MI) is a subfield of research within explainable artificial intelligence that aims to understand the internal workings of neural networks by analyzing the mechanisms present in their computations. The approach seeks to analyze neural networks in a manner similar to how binary computer programs can be reverse-engineered to understand their functions.
History
[edit]The term mechanistic interpretability was coined by Chris Olah.[1]
Early work combined various techniques such as feature visualization, dimensionality reduction, and attribution with human-computer interaction methods to analyze models like the vision model Inception v1.[2]
Key concepts
[edit]Mechanistic interpretability aims to identify structures, circuits or algorithms encoded in the weights of machine learning models.[3] This contrasts with earlier interpretability methods that focused primarily on input-output explanations.[4]
Linear representation hypothesis
[edit]
This hypothesis suggests that high-level concepts are represented as linear directions in the activation space of neural networks. Empirical evidence from word embeddings and more recent studies supports this view, although it does not hold up universally.[5][6]
Methods
[edit]Mechanistic interpretability employs causal methods to understand how internal model components influence outputs, often using formal tools from causality theory.[7]
Mechanistic interpretability, in the field of AI safety, is used to to understand and verify the behavior of complex AI systems, and to attempt to identify potential risks.[8]
References
[edit]- ^ Saphra, Naomi; Wiegreffe, Sarah (2024). Mechanistic? (PDF). BlackboxNLP workshop.
- ^ Olah, Chris; et al. (2018). "The Building Blocks of Interpretability". Distill. 3 (3). doi:10.23915/distill.00010.
- ^ "Towards automated circuit discovery for mechanistic interpretability". NeurIPS: 16318â16352. 2023.
- ^ Kästner, Lena; Crook, Barnaby (2024). "Explaining AI through mechanistic interpretability". European Journal for Philosophy of Science. 14 (4) 52. doi:10.1007/s13194-024-00614-4.
- ^ "Linguistic Regularities in Continuous Space Word Representations". NAACL: 746â751. 2013.
- ^ Park, Kiho (2024). "The Linear Representation Hypothesis and the Geometry of Large Language Models". ICML. 235: 39643â39666.
- ^ "Investigating Gender Bias in Language Models Using Causal Mediation Analysis". NeurIPS: 12388â12401. 2020. ISBN 978-1-7138-2954-6.
- ^ Sullivan, Mark (2025-04-22). "This startup wants to reprogram the mind of AIâand just got $50 million to do it". Fast Company. Retrieved 2025-05-12.
Further reading
[edit]- Nanda, Neel (2023). "Emergent Linear Representations in World Models of Self-Supervised Sequence Models". BlackNLP Workshop: 16â30. doi:10.18653/v1/2023.blackboxnlp-1.2.