中文摘要
本文提出一个利用人工智能改进人类决策的统计框架。每位人类决策者的表现均以机器预测的表现为基准进行比较。我们将部分决策者作出的诊断替换为机器学习算法给出的建议。我们利用一个全国性数据集, 将启发式频率学派方法与贝叶斯后验损失函数方法应用于异常出生检测, 该数据集包含育龄夫妇孕前检查的医生诊断及妊娠结局。我们发现, 在测试数据集上, 与仅由医生作出的诊断相比, 我们的算法具有更高的总体真阳性率和更低的假阳性率。
Abstract
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 data set of doctor diagnoses from prepregnancy checkups of reproductive-age couples and pregnancy outcomes. We find that our algorithm on a test data set results in a higher overall true positive rate and a lower false positive rate than the diagnoses made by doctors only.