用案例推理+知识图谱让AI决策更透明，适合高风险场景
ClarifAI: Enhancing AI Interpretability and Transparency through Case-Based Reasoning and Ontology-Driven Approach for Improved Decision-Making
- 结合案例推理与知识图谱，生成可理解的决策解释
- 通过结构化知识提升多领域决策可解释性
- 适合医疗、金融等对透明度要求高的应用
本文提出 ClarifAI，一种增强人工智能可解释性与透明度的新方法，旨在提升复杂决策场景下的AI可信度。该方法融合案例推理（CBR）与知识图谱驱动机制，构建系统化的解释框架，支持不同利益相关方对AI决策过程的理解需求。论文详细阐述了其理论基础、设计原则与整体架构，强调其在医疗、金融等高风险领域中的适用潜力。ClarifAI通过整合历史案例与语义知识，实现对决策逻辑的深度追溯与清晰呈现，为推动可信赖AI在关键任务中的部署提供可行路径。
原文摘要 · Abstract (English)
This Study introduces Clarity and Reasoning Interface for Artificial Intelligence(ClarifAI), a novel approach designed to augment the transparency and interpretability of artificial intelligence (AI) in the realm of improved decision making. Leveraging the Case-Based Reasoning (CBR) methodology and integrating an ontology-driven approach, ClarifAI aims to meet the intricate explanatory demands of various stakeholders involved in AI-powered applications. The paper elaborates on ClarifAI's theoretical foundations, combining CBR and ontologies to furnish exhaustive explanation mechanisms. It further elaborates on the design principles and architectural blueprint, highlighting ClarifAI's potential to enhance AI interpretability across different sectors and its applicability in high-stake environments. This research delineates the significant role of ClariAI in advancing the interpretability of AI systems, paving the way for its deployment in critical decision-making processes.
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