通过本体上下文让AI决策可解释，避免机构知识丢失
Enabling Ethical AI: A case study in using Ontological Context for Justified Agentic AI Decisions
- AI先提知识结构，专家验证修正并反馈优化模型
- 提升回答质量与效率，防止组织知识遗忘
- 适合需要可解释决策的医疗、金融等合规场景
本文提出一种人机协作的可检查语义层构建方法，用于支持可解释的智能体（Agentic AI）决策。AI从多源数据中生成候选知识结构，领域专家对其进行验证、修正和扩展，反馈用于迭代优化后续模型。该过程能有效捕捉隐性机构知识，提升响应质量与效率，并缓解组织记忆衰减问题。我们主张从事后解释转向可辩护的智能体决策，使所有决策基于显式、可检查的证据与推理，既可供专家也可供非专业人士理解。
原文摘要 · Abstract (English)
In this preprint, we present A collaborative human-AI approach to building an inspectable semantic layer for Agentic AI. AI agents first propose candidate knowledge structures from diverse data sources; domain experts then validate, correct, and extend these structures, with their feedback used to improve subsequent models. Authors show how this process captures tacit institutional knowledge, improves response quality and efficiency, and mitigates institutional amnesia. We argue for a shift from post-hoc explanation to justifiable Agentic AI, where decisions are grounded in explicit, inspectable evidence and reasoning accessible to both experts and non-specialists.
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