Document Type

Article

Publication Title

International Journal of Multidisciplinary and Current Educational Research (IJMCER)

Abstract

The interpretability of deep neural networks (DNNs) is a critical focus in artificial intelligence (AI) and machine learning (ML), particularly as these models are increasingly deployed in high-stakes applications such as healthcare, finance, and autonomous systems. In the context of these technologies, interpretability refers to the extent to which a human can understand the cause of a decision made by a model. This article evaluates various methods for assessing the interpretability of DNNs, recognizing the significant challenges posed by their complex and opaque nature. The review encompasses both quantitative metrics and qualitative evaluations, aiming to identify effective strategies that enhance model transparency without compromising performance. The structure of the article includes an exploration of quantitative metrics, such as model complexity and computational requirements, and techniques like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). It also considers qualitative evaluations, emphasizing the role of human judgment through domain expert reviews and user studies. Additionally, the article addresses the necessity of standardized benchmarks and the importance of context-specific evaluation frameworks. Through an investigation of these approaches, the treatment provides an overview of current methods and proposes future directions for improving the interpretability of DNNs, thus enhancing trust, accountability, and transparency in AI systems.

Publication Date

6-2024

Creative Commons License

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

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