A Comprehensive Review of Explainable Artificial Intelligence: Challenges, Methods, and Applications
Abstract
The rapid adoption of artificial intelligence (AI) across diverse domains has raised critical concerns about the interpretability and transparency of AI models. This paper provides a comprehensive review of Explainable Artificial Intelligence (XAI), focusing on the challenges, methods, and applications. We categorize and evaluate the existing XAI techniques, including model-agnostic and model-specific approaches, and discuss their applicability across industries such as healthcare, finance, and autonomous systems. Additionally, we highlight the ethical implications and limitations of current XAI methodologies. The review aims to serve as a resource for researchers and practitioners seeking to develop or deploy interpretable AI solutions.
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