A Neuro-Symbolic Hybrid Framework for Enhancing Explainability in AI Decision-Making Systems

In recent years, deep learning models have achieved remarkable performance in extracting complex patterns from large-scale data and have shown significant success in domains such as computer vision, natural language processing, and decision-support systems. However, the inherent "black-box" nature of these models raises critical concerns regarding transparency, reliability, and trust, particularly in high-risk applications such as medical diagnosis, autonomous driving, and security-critical systems. In contrast, symbolic artificial intelligence provides interpretable reasoning through structured logic and rule-based mechanisms, yet struggles to scale effectively in environments involving unstructured or high-dimensional data. This research proposes a hybrid neuro-symbolic framework that integrates deep learning with symbolic reasoning to enhance the explainability of AI systems while maintaining competitive performance. The proposed architecture connects neural components with symbolic inference layers, enabling the system to articulate the reasoning behind its decisions and present interpretable explanations for its outputs. Preliminary observations suggest that the hybrid approach can significantly improve model transparency and user trust without causing substantial degradation in accuracy. These findings highlight the potential of neuro-symbolic AI as a viable pathway toward developing reliable, explainable, and trustworthy intelligent systems.

کلیدواژه ها:

Hybrid Artificial Intelligence ، Deep Learning ، Symbolic AI ، Explainable AI ، Neuro-Symbolic Reasoning ، Trustworthy AI