融合神经与符号模型，让高风险领域AI既准又可解释。
Hybrid Neuro-Symbolic Models for Ethical AI in Risk-Sensitive Domains
- 用神经网络+符号逻辑结合，兼顾预测能力与可解释性。
- 在医疗、金融等场景中实现可审计的可靠决策支持。
- 适合需要合规、透明和伦理对齐的高风险应用。
部署于医疗、金融、安全等高风险领域的人工智能，不仅需具备预测准确性，还必须保证透明性、伦理一致性及符合监管要求。混合神经符号模型将神经网络的模式识别能力与符号推理的可解释性及逻辑严谨性相结合，适用于此类场景。本文综述了混合架构、伦理设计考量与部署模式，强调知识图谱与深度推理的集成、公平性规则嵌入及人类可读解释生成技术。通过医疗决策支持、金融风险管理及自主基础设施的案例研究，展示混合系统在提供可靠且可审计的AI方面的潜力。最后，提出评估协议与未来在复杂高风险环境中的扩展方向。
原文摘要 · Abstract (English)
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.
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