A Machine Learning Approach to Analyze and Support Anticorruption Policy
使用梯度提升分类器，基于巴西市政预算数据预测腐败，发现机器学习指导的定向审计比随机审计能多发现近一倍的腐败市政，对政策制定者优化审计资源有直接帮助。
Can machine learning support better governance? This study uses a tree-based, gradient-boosted classifier to predict corruption in Brazilian municipalities using budget data as predictors. The trained model offers a predictive measure of corruption, which we validate through replication and extension of previous corruption studies. Our policy simulations show that machine learning can significantly enhance corruption detection: Compared to random audits, a machine-guided targeted policy could detect almost twice as many corrupt municipalities for the same audit rate.