用可解释模型替代黑箱模型，提升就业预测透明度与公平性。
Transparent and Fair Profiling in Employment Services: Evidence from Switzerland
- 采用可解释的提升机模型，兼顾预测性能与透明度。
- 模型稀疏性与特征平滑使公平性提升，性能损失极小。
- 适合关注算法公平性的公共政策制定者使用。
长期失业是求职者和公共就业服务共同面临的挑战。统计分析工具被越来越多地用于预测长期失业风险，但部分工具为黑箱机器学习模型，存在透明度与公平性问题。本文基于瑞士行政数据，对比传统统计模型、可解释模型与黑箱模型在预测性能、可解释性及公平性方面的表现。研究发现，可解释提升机模型（Explainable Boosting Machines）的预测性能接近最优黑箱模型。通过模型稀疏化、特征平滑与公平性缓解技术，可在仅轻微牺牲性能的前提下显著提升透明度与公平性。结果表明，可解释模型能提供既可信又可问责的替代方案，无需以性能为代价。
原文摘要 · Abstract (English)
Long-term unemployment (LTU) is a challenge for both jobseekers and public employment services. Statistical profiling tools are increasingly used to predict LTU risk. Some profiling tools are opaque, black-box machine learning models, which raise issues of transparency and fairness. This paper investigates whether interpretable models could serve as an alternative, using administrative data from Switzerland. Traditional statistical, interpretable, and black-box models are compared in terms of predictive performance, interpretability, and fairness. It is shown that explainable boosting machines, a recent interpretable model, perform nearly as well as the best black-box models. It is also shown how model sparsity, feature smoothing, and fairness mitigation can enhance transparency and fairness with only minor losses in performance. These findings suggest that interpretable profiling provides an accountable and trustworthy alternative to black-box models without compromising performance.
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