Neuro-symbolic AI for transparent decision-making in high-stakes domains

Abstract

Demand for explainable AI systems that strike a balance between interpretability and predictive accuracy has increased due to the growing use of machine learning models in high-stakes fields like healthcare and legal decision-making. Despite their strength, traditional neural networks frequently lack transparency, which reduces their reliability in crucial applications. We address this by putting forth a novel neuro-symbolic AI framework that combines symbolic reasoning and neural feature extraction to produce explainable decision-making systems. Our method uses a neural encoder to map high-dimensional features to a symbolic knowledge base, where decision outputs are governed by logical rules, from complex inputs (such as legal texts or medical images). Predictions made using this hybrid architecture are guaranteed to be both data-driven and interpretable using rules that are easy for humans to understand. To optimize the symbolic component and allow end-to-end training while maintaining interpretability, we present a differentiable rule-learning mechanism. We test our framework on benchmark datasets, such as legal text classification and medical diagnostics (e.g., ChestX-ray14), and show competitive accuracy and superior explainability when compared to black-box models like transformers. Our findings demonstrate that the suggested model reduces bias by explicitly encoding domainspecific constraints in addition to offering concise, logical justifications for its choices. With implications for user trust and regulatory compliance, this work advances the field of explainable AI by providing a scalable solution for implementing reliable machine learning systems in safety-critical domains.