验证了可解释AI框架在多领域的一致性与适应性。
Extended Empirical Validation of the Explainability Solution Space
- 用城市资源分配系统验证框架跨领域适用性
- 发现不同治理角色下解释方法排名稳定不变
- 适合需要跨系统设计可解释AI的决策者
本技术报告通过跨领域评估，扩展验证了可解释性解决方案空间（ESS）的有效性。初始验证聚焦于员工流失预测，本研究引入异构智能城市资源分配系统，展示该框架的通用性与领域无关性。第二项案例研究在多利益相关方治理条件下，整合表格、时间序列与地理空间数据。两个场景均明确量化定位了代表性XAI方法族的位置。结果表明，ESS排名并非领域特有，而是能系统适应治理角色、风险特征与利益相关方配置。研究证实ESS可作为跨社会技术系统中可解释人工智能策略设计的通用化决策支持工具。
原文摘要 · Abstract (English)
This technical report provides an extended validation of the Explainability Solution Space (ESS) through cross-domain evaluation. While initial validation focused on employee attrition prediction, this study introduces a heterogeneous intelligent urban resource allocation system to demonstrate the generality and domain-independence of the ESS framework. The second case study integrates tabular, temporal, and geospatial data under multi-stakeholder governance conditions. Explicit quantitative positioning of representative XAI families is provided for both contexts. Results confirm that ESS rankings are not domain-specific but adapt systematically to governance roles, risk profiles, and stakeholder configurations. The findings reinforce ESS as a generalizable operational decision-support instrument for explainable AI strategy design across socio-technical systems.
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