Abstract
As artificial intelligence (AI) models become increasingly complex, understanding their decision-making processes is essential for building trust and ensuring accountability. Explainable AI (XAI) is an emerging field focused on improving the interpretability of AI models, particularly deep neural networks. This article explores the importance of explainability in AI and the various approaches to developing explainable neural networks. It discusses the challenges and benefits of implementing explainable AI techniques, with a focus on enhancing transparency, providing insights into model behavior, and fostering the responsible use of AI in high-stakes applications. The article also examines future directions and innovations in explainable neural networks, emphasizing their potential to improve AI applications in healthcare, finance, and other critical sectors
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