Robustness of Multiple Indicators in Automated Screening Systems for Deception Detection

• 成果类型: Article
• 署名作者: Twyman, Nathan W.; Proudfoot, Jeffrey Gainer; Schuetzler, Ryan M.; Elkins, Aaron C.; Derrick, Douglas C.
• 署名单位: University of Missouri System; Missouri University of Science & Technology; University of Arizona; Bentley University; University of Nebraska System; California State University System; San Diego State University; University of Nebraska System; University of Nebraska System
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2015.1138569
• 发表日期: 2015
• 页码: 215-245
• 关键词: physical countermeasures reduce; concealed information test; complex trial protocol; guilty knowledge; psychophysiological detection; mental countermeasures; vocal expression; design science; polygraph; accuracy

摘要

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.