Neuro-Symbolic Self-Explanatory AI (NS-XAI): A Real-Time Framework for Transparent and Adaptive Decision-Making

Artificial Intelligence (AI) is transforming industries such as healthcare, finance, cybersecurity, and autonomous systems, where real-time and transparent decision-making is crucial. However, most Explainable AI (XAI) techniques provide post-hoc explanations, meaning they explain decisions only after they are made. This delay reduces trust and makes AI difficult to use in time-sensitive applications like fraud detection, medical diagnosis, and autonomous driving. To overcome these challenges, we propose Neuro-Symbolic Self-Explanatory AI (NS-XAI), a novel approach that enables AI to explain its decisions instantly while making them. NS-XAI integrates Neural AI, which learns complex patterns from data, with Symbolic AI, which generates human-readable rules to justify decisions. Additionally, it features a self-explanatory layer that continuously refines explanations based on past decisions, ensuring consistency and improving interpretability. This system enhances fairness by helping users understand not only what decision was made but also why and how it was reached. Unlike traditional methods, NS-XAI operates in real-time, making it ideal for applications where instant and interpretable AI decisions are required. In fraud detection, it can immediately explain why a transaction is flagged as suspicious; and in healthcare, it can justify diagnoses with clear reasoning. By bridging the gap between AI accuracy and explainability, NS-XAI significantly reduces bias, enhances trust, and improves decision reliability Experiments show that NS-XAI is 10 times faster than existing explainability models while maintaining high accuracy. By offering real-time, clear, and adaptive explanations, this system makes AI more transparent, fair, and user-friendly, paving the way for the next generation of responsible and interpretable AI applications.