Interpretable machine learning with SHAP predicts honest behavior from personality traits and physiological data.

Yu Meng, Yihan Chen, Zhitong Zhang, Hanxi Huang, Qiaolin Chen, Zili Peng, Qunfei Zheng, Yiming Liu

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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8 authors.

Yu MengCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China. myucrm@126.com.

Yihan ChenCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China.

Zhitong ZhangCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China.

Hanxi HuangCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China.

Qiaolin ChenCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China.

Zili PengCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China.

Qunfei ZhengCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China.

Yiming LiuCollege of Flight Technology, Civil Aviation Flight University of China, Deyang, 618307, Sichuan, China.

Funding

Civil Aviation Administration of China MHJY2024040Civil Aviation Flight University of China 25CAFUC03007

6 · The paper itself

Abstract

This paper investigates the extent to which dishonest behavior can be predicted using behavioral personality traits and physiological information. An interpretable machine learning approach, SHapley Additive exPlanations (SHAP) analysis, is employed to identify the most influential predictors. Drawing on data from the HEXACO personality inventory and physiological measures (including skin conductance activity and heart rate variability), we construct several machine learning models: a personality-based model, a physiology-based model, and a combined model. The results indicate that the AdaBoost personality-based model achieved the strongest predictive performance (AUC = 0.836), outperforming models based solely on physiological indicators. SHAP analysis further reveals that Openness to Experience and Extraversion are the most salient positive predictors of dishonest behavior. Among the physiological features, very-low-frequency components of heart rate variability and finger pulse amplitude emerge as meaningful predictors. Overall, this study offers preliminary insights into predictors of immediate dishonest behavior and suggests that integrating personality traits with physiological signals is a feasible approach for predictive modeling, providing a methodological reference for assessing individual honesty.

Indexed as

DeceptionMachine LearningPersonalityBoosting Machine Learning AlgorithmsGalvanic Skin ResponseHeart RateHumansMalePredictive Learning ModelsDishonest behaviorMachine learningPersonality traitsPhysiological measures

Identifiers

PMID41826478

DOI10.1038/s41598-026-41677-y

PMCPMC13111637

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LicenceCC BY-NC-ND

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