Towards Trustworthy AI: A Framework for Explainable and Robust Deep Learning in Critical Systems

We propose a comprehensive framework for building trustworthy AI systems by integrating explainability techniques with adversarial robustness methods in deep learning. This work addresses critical concerns regarding bias, transparency, and reliability, particularly in high-stakes applications such as autonomous driving and financial fraud detection, fostering greater confidence in AI deployments.