ArticleOpen Access http://dx.doi.org/10.26855/jamc.2025.09.001
Interpretability Bottleneck Breakthrough Method for Deep Learning Algorithms
Jian Sun1,*, Yizheng Xu2, Yansong Li3
1Iowa State University, Ames, Iowa 50011, USA.
2University of Malaya, Kuala Lumpur 50603, Malaysia.
3Zhengzhou Police College, Zhengzhou 450000, Henan, China.
*Corresponding author:Jian Sun
Published: August 20,2025
Abstract
Deep learning models have achieved remarkable success across various domains, yet their opaque decision-making processes hinder their deployment in critical applications such as healthcare and finance. This paper examines fundamental challenges in model interpretability, including the unclear semantics of learned features, limitations of current explanation techniques, and the inherent tension between accuracy and explainability. We analyze prevailing interpretation methods, including attention mechanisms, feature attribution approaches, and surrogate models, identifying key shortcomings in their ability to provide meaningful explanations. To address these limitations, we introduce several innovative approaches: self-explaining neural architectures that generate explanations alongside predictions, adaptive interpretation frameworks that adjust to different contexts, and collaborative systems that combine AI analysis with human expertise. Experimental results demonstrate that our proposed methods significantly enhance model transparency while preserving predictive performance. These contributions advance both theoretical understanding of deep learning interpretability and practical methodologies for developing more trustworthy AI systems. The findings provide valuable insights for researchers and practitioners seeking to implement explainable AI solutions in real-world scenarios where decision accountability is paramount.
Keywords
Interpretability in deep learning; Feature attribution; Self-explaining models; Attention mechanisms; Human-AI collaboration
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How to cite this paper
Interpretability Bottleneck Breakthrough Method for Deep Learning Algorithms
How to cite this paper: Jian Sun, Yizheng Xu, Yansong Li. (2025) Interpretability Bottleneck Breakthrough Method for Deep Learning Algorithms. Journal of Applied Mathematics and Computation, 9(3), 150-154.
DOI: http://dx.doi.org/10.26855/jamc.2025.09.001