Neuro-Symbolic AI for Explainable and Reliable Intelligent Systems
Keywords:
Neuro-Symbolic AI, Explainable AI, Symbolic Reasoning, Neural Networks, Trustworthy AI, Knowledge Representation, Intelligent Systems.Abstract
Artificial Intelligence (AI) systems have achieved significant progress in areas such as classification, prediction, natural language processing, computer vision, and autonomous decision-making. However, many modern neural models operate as black-box systems, making it difficult to understand how a particular prediction or decision is generated. Limited interpretability, weak formal reasoning, sensitivity to distribution changes, and difficulties in verifying model behavior create challenges for deploying AI in safety-critical and high-stakes environments. Neuro-Symbolic Artificial Intelligence (Neuro-Symbolic AI) provides a promising approach by integrating the pattern-learning capabilities of neural networks with the structured representation and reasoning capabilities of symbolic AI. Recent research identifies explainability, trustworthiness, reasoning, robustness, and verification as important areas for Neuro-Symbolic AI development. This paper presents a conceptual Neuro-Symbolic AI framework for developing explainable and reliable intelligent systems. The proposed methodology combines neural perception, knowledge representation, symbolic reasoning, decision generation, and explanation mechanisms. The framework is designed to improve transparency by producing reasoning traces and rule-based explanations alongside predictive outputs. A multi-dimensional evaluation strategy based on predictive performance, explainability, robustness, consistency, and reasoning accuracy is proposed. The study demonstrates how the integration of neural learning and symbolic reasoning can provide a stronger foundation for trustworthy intelligent systems while addressing limitations associated with purely data-driven models.
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Copyright (c) 2025 Marissa E. Wechsler (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
