Explainable AI in Neural Networks: Investigating techniques for explaining the decisions and behaviors of neural network models to improve transparency and trust

Abstract

Explainable Artificial Intelligence (XAI) has emerged as a crucial area of research, especially in complex models like Neural Networks (NNs), where understanding model decisions is challenging. This paper provides a comprehensive review of techniques for enhancing the explainability of NNs. We first discuss the importance of explainability in AI, highlighting its significance in ensuring trust and facilitating decision-making. Next, we delve into various methods for explaining NNs, including feature visualization, attribution methods, and model distillation. We also explore the challenges and future directions in XAI, emphasizing the need for interpretable models in critical applications. Through this paper, we aim to provide researchers and practitioners with a deep understanding of XAI techniques in NNs, fostering the development of transparent and trustworthy AI systems.