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 crucial focus within artificial intelligence and machine learning, particularly as these models are increasingly used in high-stakes applications such as healthcare, finance, and autonomous driving. This article explores the impact of architectural design choices on the interpretability of DNNs, emphasizing the importance of transparency, trust, and accountability in AI systems. By presenting case studies and experimental results, the article highlights how different architectural elements—such as layer types, network depth, connectivity patterns, and attention mechanisms—affect model interpretability and performance. The discussion is structured into three main sections: real-world applications, architectural trade-offs, and tools and techniques in practice. In healthcare, for example, interpretability techniques like heatmaps enhance diagnostic model transparency, aiding clinical decision-making and improving patient outcomes. In finance, methods such as LIME and SHAP provide clear explanations for credit scoring models, facilitating regulatory compliance and risk assessment. For autonomous driving, the article examines how interpretability ensures safety and reliability, fostering public trust. Through a comprehensive analysis of attention mechanisms and a comparison of convolutional versus recurrent layers, the article offers insights into balancing performance and interpretability. Additionally, it reviews practical tools like LIME and SHAP, demonstrating their effectiveness in enhancing model transparency. The findings underscore the necessity of tailored interpretability solutions to ensure the broader acceptance and effective utilization of DNNs in critical real-world scenarios.

Publication Date

6-2024

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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