Document Type
Article
Publication Title
International Journal of Multidisciplinary and Current Educational Research (IJMCER)
Abstract
The interpretability of Deep Neural Networks (DNNs) has become a critical focus in artificial intelligence and machine learning, particularly as DNNs are increasingly used in high-stakes applications like healthcare, finance, and autonomous driving. Interpretability refers to the extent to which humans can understand the reasons behind a model's decisions, which is essential for trust, accountability, and transparency. However, the complexity and depth of DNN architectures often compromise interpretability as these models function as "black boxes." This article reviews key architectural elements of DNNs that affect their interpretability, aiming to guide the design of more transparent and trustworthy models. The primary objective is to examine different layer types, network depth and complexity, connectivity patterns, and attention mechanisms to provide a comprehensive understanding of how these architectural components can enhance the interpretability of DNNs. Layer types, including convolutional, recurrent, and attention layers, have unique properties affecting model interpretability. Convolutional layers are fundamental for image recognition tasks, recurrent layers handle sequential data, and attention layers improve model performance by selectively focusing on relevant parts of the input. Network depth and complexity significantly impact interpretability, with shallow networks being more interpretable but less powerful on complex tasks compared to deep networks. Connectivity patterns also play a crucial role; while fully connected layers offer high flexibility, they pose interpretability challenges due to dense connections, whereas structured connectivity patterns like residual networks provide clearer information flow. Attention mechanisms further enhance interpretability by dynamically highlighting the most relevant parts of the input data. This review helps researchers and practitioners identify strategies to design DNNs that are both performant and interpretable, contributing to the advancement of trustworthy AI applications.
Publication Date
6-2024
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Barnes, Emily and Hutson, James, "Architectural Elements Contributing to Interpretability of Deep Neural Networks (DNNs)" (2024). Faculty Scholarship. 642.
https://digitalcommons.lindenwood.edu/faculty-research-papers/642