Neuro-Symbolic AI for Explainable Reasoning

Abstract

Neuro-symbolic AI represents a hybrid paradigm that bridges neural networks’ pattern recognition capabilities with the structured, interpretable reasoning of symbolic AI. This integration addresses the need for explainable reasoning—a critical foundation for deploying AI in high-stakes domains such as healthcare, finance, and legal systems. This paper explores the motivations, architecture, methodologies, and empirical findings that shape neuro-symbolic AI as a means for transparent reasoning. We first outline the conceptual underpinnings of neuro-symbolic integration: neural networks excel at processing perceptual data but struggle with structured reasoning, while symbolic systems provide logic and interpretability yet falter in noisy, data-rich contexts. By combining the two, neuro-symbolic AI aims to achieve both accuracy and explainability. The literature review surveys key hybrid models such as Explainable Neural-Symbolic Learning (X-NeSyL), which fuses deep representations with domain expert knowledge graphs, and MRKL systems which integrate LMs with symbolic reasoning modules. These models demonstrate how symbolic components enable traceable reasoning paths and improve interpretability. Our research methodology proposes constructing hybrid models where neural perception feeds structured symbolic pipelines. We assess their performance and explainability using tasks like image classification with rule-based validation or question answering over symbolic facts. Metrics include accuracy, inference transparency, and humanalignment of explanations. Results from existing studies show improved interpretability without significant loss in predictive performance. For instance, X-NeSyL enhances classification with expert-aligned explanations. However, challenges include architectural complexity, training inefficiencies, and scalability limits. We conclude that neuro-symbolic systems offer a compelling path toward trustworthy AI by embedding symbolic traceability into neural computation. Future work should focus on modular architectures, scalable hybrid frameworks, and standardized explainability benchmarks to foster broader adoption.