Neural Networks and Explainable AI: Bridging the Gap between Models and Interpretability

Abstract

Abstract: In this paper, we explore the intersection of neural networks and explainable artificial intelligence (XAI), aiming to bridge the gap between complex model architectures and interpretability. While neural networks have demonstrated remarkable performance across various tasks, their inherently black-box nature poses challenges in understanding the underlying decision-making process. We propose novel approaches and methodologies to enhance the interpretability of neural networks, thereby facilitating trust, transparency, and accountability in AI systems. Through a comprehensive review of existing literature and methodologies, we identify key challenges and opportunities in the field of XAI. Our study emphasizes the importance of developing interpretable neural network architectures, incorporating explainability mechanisms during model training, and leveraging post-hoc interpretability techniques. We also highlight the significance of domain-specific interpretability and the ethical implications of AI decision-making. By addressing these challenges and advancing the state-of-the-art in XAI, we aim to foster greater trust and acceptance of neural network models in real-world applications, ultimately enabling more informed and responsible AI-driven decision-making processes.