EXPLAINABLE HYBRID MACHINE LEARNING PARADIGM FOR TRUSTWORTHY ADVERSARIAL DEFENSE

The accuracy of user-generated suggestions is critical, especially in decision-making processes where AI systems play an important role. Explainable AI (XAI) plays a vital role in decision making process as a mechanism for assessing and verifying the validity of these inputs, ensuring that AI models stay correct and reliable. This focus concentrate on how to apply XAI approaches to determine if users provide correct suggestions or attempt to deceive the system, hence improving the accuracy of AI-driven decision-making. This paper recommends a novel hybrid model that incorporates numerous sophisticated techniques, including Noisy Student self-training, Generative Adversarial Networks (GANs), and XAI. The Noisy Student technique enhances model resilience by self-training on noisy data, whereas GANs provide genuine adversarial instances for data augmentation. Ensemble learning and robust optimization techniques improve model resilience, guaranteeing that it can withstand and respond appropriately to hostile inputs. Generally, XAI techniques contribute transparency, allowing for the discovery and mitigation of misleading inputs by clarifying model predictions. This hybrid method not only improves the robustness and accuracy of AI models, but it also sets a new bar for openness in AI systems, addressing both technical and ethical concerns in modern AI applications.