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.

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.