解释不可解释之物：金融领域可解释人工智能的系统性综述
Explaining the Unexplainable: A Systematic Review of Explainable AI in Finance
AI总结：
本文针对金融领域XAI应用展开系统性综述，通过文献计量与内容分析揭示主题集群、常用策略等，发现事后可解释技术占主导，强调多学科方法的必要性并指出当前系统缺陷。
摘要：
试图在准确性和透明度之间取得平衡的从业者与研究者将可解释人工智能（XAI）置于金融领域的核心位置。本文对金融领域XAI应用的变化场景进行了全面概述，涵盖特定领域的实施、方法学进展及研究趋势图谱。通过文献计量和内容分析，我们发现了金融行业中的主题集群、重要研究及最常用的可解释性策略。结果显示，现有研究高度依赖事后可解释性技术，其中注意力机制、特征重要性分析和SHAP是最常用的技术。本综述强调了融合金融知识与改进的可解释性范式的多学科方法的必要性，并揭示了当前XAI系统的重大缺陷。
英文摘要：
Practitioners and researchers trying to strike a balance between accuracy and transparency center Explainable Artificial Intelligence (XAI) at the junction of finance. This paper offers a thorough overview of the changing scene of XAI applications in finance together with domain-specific implementations, methodological developments, and trend mapping of research. Using bibliometric and content analysis, we find topic clusters, significant research, and most often used explainability strategies used in financial industries. Our results show a substantial dependence on post-hoc interpretability techniques; attention mechanisms, feature importance analysis and SHAP are the most often used techniques among them. This review stresses the need of multidisciplinary approaches combining financial knowledge with improved explainability paradigms and exposes important shortcomings in present XAI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
