用可解释模型+大模型+专家参与，让公益项目评估既准又可信。
Integrating Transparent Models, LLMs, and Practitioner-in-the-Loop: A Case of Nonprofit Program Evaluation
- 透明决策树结合大模型生成可理解的个体评估结果。
- 专家全程参与使分析贴合实际需求，提升可用性。
- 适合需要可解释性与落地性的公益、公共部门AI应用。
公共和非营利组织常因现有AI模型不透明而犹豫采用，尽管其分析多基于整体趋势而非具体案例的行动建议。本研究测试了一种从业者参与的工作流程：将可解释的决策树模型与大语言模型（LLMs）结合，以提升预测准确性、可解释性及实践洞察力。基于一项持续进行的大学成功项目数据，我们构建了可解释的决策树以识别关键预测因子，并将每棵树的结构输入至LLM，使其生成基于透明模型的案例级预测。从业务人员全程参与特征工程、模型设计、解释审核与可用性评估，确保一线经验贯穿分析全过程。结果表明，整合透明模型、大模型与从业者反馈，可实现准确、可信且可操作的案例级评估，为公共与非营利领域负责任地采纳AI提供了可行路径。
原文摘要 · Abstract (English)
Public and nonprofit organizations often hesitate to adopt AI tools because most models are opaque even though standard approaches typically analyze aggregate patterns rather than offering actionable, case-level guidance. This study tests a practitioner-in-the-loop workflow that pairs transparent decision-tree models with large language models (LLMs) to improve predictive accuracy, interpretability, and the generation of practical insights. Using data from an ongoing college-success program, we build interpretable decision trees to surface key predictors. We then provide each tree's structure to an LLM, enabling it to reproduce case-level predictions grounded in the transparent models. Practitioners participate throughout feature engineering, model design, explanation review, and usability assessment, ensuring that field expertise informs the analysis at every stage. Results show that integrating transparent models, LLMs, and practitioner input yields accurate, trustworthy, and actionable case-level evaluations, offering a viable pathway for responsible AI adoption in the public and nonprofit sectors.
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