This paper proposes a statistical framework of using artificial intelligence to improve human decision making. The performance of each human decision maker is benchmarked against that of machine predictions. We replace the diagnoses made by a subset of the decision makers with the recommendation from the machine learning algorithm. We apply both a heuristic frequentist approach and a Bayesian posterior loss function approach to abnormal birth detection using a nationwide dataset of doctor diagnoses from prepregnancy checkups of reproductive age couples and pregnancy outcomes. We find that our algorithm on a test dataset results in a higher overall true positive rate and a lower false positive rate than the diagnoses made by doctors only.

翻译：本文提出一个利用人工智能改进人类决策的统计框架。我们将每位人类决策者的表现与机器预测进行基准比较。对于部分决策者的诊断结果，我们采用机器学习算法的推荐予以替代。基于全国范围的育龄夫妇孕前检查医生诊断与妊娠结局数据集，我们同时采用启发式频率学派方法和贝叶斯后验损失函数方法进行异常妊娠检测。研究发现，在测试数据集上，我们的算法相较于纯医生诊断实现了更高的总体真阳性率与更低的假阳性率。