Sber Innovation and Research, Sberbank of Russia, Moscow, 117997, Russian Federation.
Internal Security Department, Sberbank of Russia, Moscow, 117997, Russian Federation.
Sci Rep. 2023 Apr 17;13(1):5522. doi: 10.1038/s41598-023-31775-6.
Classical polygraph screenings are routinely used by critical businesses such as banking, law enforcement agencies, and federal governments. A major concern of scientific communities is that screenings are prone to errors. However, screening errors are not only due to the method, but also due to human (polygraph examiner) error. Here we show application of machine learning (ML) to detect examiner errors. From an ML perspective, we trained an error detection model in the absence of labeled errors. From a practical perspective, we devised and tested successfully a second-opinion tool to find human errors in examiners' conclusions, thus reducing subjectivity of polygraph screenings. We report novel features that uplift the model's accuracy, and experimental results on whether people lie differently on different topics. We anticipate our results to be a step towards rethinking classical polygraph practices.
经典测谎仪筛查通常被银行、执法机构和联邦政府等关键行业使用。科学界主要关注的问题是筛查容易出错。然而，筛查错误不仅是由于方法，还由于人为（测谎仪检查者）错误。在这里，我们展示了机器学习 (ML) 的应用，以检测检查者的错误。从机器学习的角度来看，我们在没有标记错误的情况下训练了一个错误检测模型。从实际的角度来看，我们成功地设计和测试了一个第二意见工具，以找到检查者结论中的人为错误，从而减少测谎筛查的主观性。我们报告了提高模型准确性的新特征，并报告了关于人们在不同主题上说谎方式是否不同的实验结果。我们预计我们的结果将是重新思考经典测谎实践的一步。
