Robustness of Multiple Indicators in Automated Screening Systems for Deception Detection


automated screening systems, deception countermeasures, deception detection, design science, human–computer interaction, human risk assessment, human screening.


This study investigates the effectiveness of an automatic system for detection of deception by individuals with the use of multiple indicators of such potential deception. Deception detection research in the information systems discipline has postulated increased accuracy through a new class of screening systems that automatically conduct interviews and track multiple indicators of deception simultaneously. Understanding the robustness of this new class of systems and the limitations of its theoretical improved performance is important for refinement of the conceptual design. The design science proof-of-concept study presented here implemented and evaluated the robustness of these systems for automated screening for deception detection. A large experiment was used to evaluate the effectiveness of a constructed multiple-indicator system, both under normal conditions and with the presence of common types of countermeasures (mental and physical). The results shed light on the relative strength and robustness of various types of deception indicators within this new context. The findings further suggest the possibility of increased accuracy through the measurement of multiple indicators if classification algorithms can compensate for human attempts to counter effectiveness.

Original Publication Citation

Twyman, N. T., Proudfoot, J. G., Schuetzler, R., Elkins, A. C., & Derrick, D. C. (2015). Robustness of multiple indicators in controlled, automated deception detection interviews. Journal of Management Information Systems, 32(4), pp. 215–245.

Document Type

Peer-Reviewed Article

Publication Date


Permanent URL


Journal of Management Information Systems




Marriott School of Business


Information Systems

University Standing at Time of Publication

Assistant Professor