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. > >
Credibility assessment is an area in which information systems research can make a major impact. This paper reports on two studies investigating a system solution for automatic, noninvasive detection of rigidity for automated interviewing. Kinesic rigidity has long been a phenomenon of interest in the credibility assessment literature, but until now was infeasible as a veracity indicator in practical use cases. An initial study unexpectedly revealed the occurrence of rigidity in a highly controlled concealed information test setting, prompting the design and implementation of an automated rigidity detection system for interviewing. A unique experimental evaluation supported the system concept. The results of the second study confirmed the kinesic rigidity found in the first, and provided further theoretical insights explaining the rigidity phenomenon. Although additional research is needed, the evidence from this investigation suggests that credibility assessment can benefit from a rigidity detection system.
Screening individuals for concealed information has traditionally been the purview of professional interrogators investigating crimes. However, the ability to detect when a person is hiding important information would have high value in many other applications if results could be reliably obtained using an automated and rapid interviewing system. Unfortunately, this ideal has thus far been stymied by practical limitations and inadequate scientific control in current interviewing systems. This study proposes a new class of systems, termed autonomous scientifically controlled screening systems (ASCSS), designed to detect individuals’ purposely hidden information about target topics of interest. These hidden topics of interest could cover a wide range, including knowledge of concealed weapons, privacy violations, fraudulent organizational behavior, organizational security policy violations, preemployment behavioral intentions, organizational insider threat, leakage of classified information, or even consumer product use information. ASCSS represent a systematic synthesis of structured interviewing, orienting theory, defensive response theory, noninvasive psychophysiological measurement, and behavioral measurement. To evaluate and enhance the design principles, we built a prototype automated screening kiosk system and configured it for a physical security screening scenario in which participants constructed and attempted to smuggle a fake improvised explosive device. The positive results provide support for the proposition that ASCSS may afford more widespread application of credibility assessment screening systems.