A Survey on Explainable Artificial Intelligence (XAI) in Deep Learning Systems
DOI:
https://doi.org/10.31305/rrijm.2024.v09.n06.047Keywords:
Explainable Artificial Intelligence, Deep Learning, Interpretability, SHAP, LIME, Grad-CAM, Model Transparency, Trustworthy AIAbstract
Artificial Intelligence has rapidly evolved over the past decade, with deep learning models achieving remarkable success in complex tasks such as medical diagnosis, natural language processing, financial prediction, and autonomous navigation. Despite their high performance, these models often operate as opaque systems whose internal decision-making processes are difficult for humans to understand. This lack of transparency has led to concerns regarding trust, accountability, bias, and ethical compliance. Explainable Artificial Intelligence (XAI) has emerged as a critical research area aimed at addressing these limitations by providing interpretability and insight into model behavior. This survey explores the foundations of XAI, categorizes existing explanation methods, reviews significant literature contributions, discusses practical applications, highlights current challenges, and outlines future research directions. The paper emphasizes the importance of XAI in enabling reliable and responsible AI deployment in safety-critical and socially sensitive domains.
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This is an open access article under the CC BY-NC-ND license Creative Commons Attribution-Noncommercial 4.0 International (CC BY-NC 4.0).
