A Survey on Neural Network Interpretability

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g., algorithmic discrimination. Moreover, interpretability...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computational intelligence Vol. 5; no. 5; pp. 726 - 742
Main Authors: Zhang, Yu, Tino, Peter, Leonardis, Ales, Tang, Ke
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.10.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Artificial neural networks
Decision trees
Deep learning
inter-pretability
Machine learning
Neural networks
Reliability
survey
Task analysis
Taxonomy
Training
ISSN:2471-285X
Online Access:Get full text
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Summary:Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g., algorithmic discrimination. Moreover, interpretability is a desired property for deep networks to become powerful tools in other research fields, e.g., drug discovery and genomics. In this survey, we conduct a comprehensive review of the neural network interpretability research. We first clarify the definition of interpretability as it has been used in many different contexts. Then we elaborate on the importance of interpretability and propose a novel taxonomy organized along three dimensions: type of engagement (passive vs. active interpretation approaches), the type of explanation, and the focus (from local to global interpretability). This taxonomy provides a meaningful 3D view of distribution of papers from the relevant literature as two of the dimensions are not simply categorical but allow ordinal subcategories. Finally, we summarize the existing interpretability evaluation methods and suggest possible research directions inspired by our new taxonomy.
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ISSN:2471-285X
DOI:10.1109/TETCI.2021.3100641