Document Type
Article
Publication Title
International Journal of Multidisciplinary and Current Educational Research
Abstract
The interpretability and explainability of deep neural networks (DNNs) are paramount in artificial intelligence (AI), especially when applied to high-stakes fields such as healthcare, finance, and autonomous driving. The need for this study arises from the growing integration of AI into critical areas where transparency, trust, and ethical decision-making are essential. This paper explores the impact of architectural design choices on DNN interpretability, focusing on how different architectural elements like layer types, network depth, connectivity patterns, and attention mechanisms affect model transparency. Methodologically, the study employs a comprehensive review of case studies and experimental results to analyze the balance between performance and interpretability in DNNs. It examines real-world applications to demonstrate the importance of interpretability in sectors like healthcare, finance, and autonomous driving. The study also reviews practical tools such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to assess their effectiveness in enhancing model transparency. The results underscore that interpretability facilitates better decision-making, accountability, and compliance with regulatory standards. For instance, using SHAP in environmental monitoring helps policymakers understand the key drivers of air quality, leading to informed interventions. In education, LIME aids educators in personalizing learning by highlighting factors influencing student performance. The findings also reveal that incorporating attention mechanisms and hybrid model architectures can significantly improve interpretability without compromising performance.
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, "Navigating the Complexities of AI: The Critical Role of Interpretability and Explainability in Ensuring Transparency and Trust" (2024). Faculty Scholarship. 643.
https://digitalcommons.lindenwood.edu/faculty-research-papers/643