Hybrid Neuro-Symbolic Models for Ethical AI in Risk-Sensitive Domains

TL;DR

This paper reviews hybrid neuro-symbolic AI models that combine neural networks and symbolic reasoning to enhance transparency, ethical compliance, and reliability in high-stakes domains like healthcare and finance.

Contribution

It provides a comprehensive survey of hybrid architectures, ethical considerations, and deployment strategies for risk-sensitive AI applications, with case studies and future research directions.

Findings

Hybrid models improve interpretability and accountability.

Integration of knowledge graphs enhances reasoning capabilities.

Case studies demonstrate reliable performance in critical domains.

Abstract

Artificial intelligence deployed in risk-sensitive domains such as healthcare, finance, and security must not only achieve predictive accuracy but also ensure transparency, ethical alignment, and compliance with regulatory expectations. Hybrid neuro symbolic models combine the pattern-recognition strengths of neural networks with the interpretability and logical rigor of symbolic reasoning, making them well-suited for these contexts. This paper surveys hybrid architectures, ethical design considerations, and deployment patterns that balance accuracy with accountability. We highlight techniques for integrating knowledge graphs with deep inference, embedding fairness-aware rules, and generating human-readable explanations. Through case studies in healthcare decision support, financial risk management, and autonomous infrastructure, we show how hybrid systems can deliver reliable and auditable AI. Finally, we outline evaluation protocols and future directions for scaling neuro symbolic frameworks in complex, high stakes environments.