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• W4366188894 abstract "Abstract 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." @default.
• W4366188894 created "2023-04-19" @default.
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• W4366188894 date "2023-04-17" @default.
• W4366188894 modified "2023-09-30" @default.
• W4366188894 title "Building a second-opinion tool for classical polygraph" @default.
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• W4366188894 doi "https://doi.org/10.1038/s41598-023-31775-6" @default.
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• W4366188894 hasPublicationYear "2023" @default.
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