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.
Despite the improving accuracy of agent-based expert systems, human expert users aided by these systems have not improved their accuracy. Self-affirmation theory suggests that human expert users could be experiencing threat, causing them to act defensively and ignore the system's conflicting recommendations. Previous research has demonstrated that affirming an individual in an unrelated area reduces defensiveness and increases objectivity to conflicting information. Using an affirmation manipulation prior to a credibility assessment task, this study investigated if experts are threatened by counterattitudinal expert system recommendations. For our study, 178 credibility assessment experts from the American Polygraph Association (n = 134) and the European Union's border security agency Frontex (n = 44) interacted with a deception detection expert system to make a deception judgment that was immediately contradicted. Reducing the threat prior to making their judgments did not improve accuracy, but did improve objectivity toward the system. This study demonstrates that human experts are threatened by advanced expert systems that contradict their expertise. As more and more systems increase integration of artificial intelligence and inadvertently assail the expertise and abilities of users, threat and self-evaluative concerns will become an impediment to technology acceptance.
We have created an automated kiosk that uses embodied intelligent agents to interview individuals and detect changes in arousal, behavior, and cognitive effort by using psychophysiological information systems. In this paper, we describe the system and propose a unique class of intelligent agents, which are described as Special Purpose Embodied Conversational Intelligence with Environmental Sensors(SPECIES). SPECIES agents use heterogeneous sensors to detect human physiology and behavior during interactions, and they affect their environment by influencing human behavior using various embodied states (i.e., gender and demeanor), messages, and recommendations. Based on the SPECIES paradigm, we present three studies that evaluate different portions of the model, and these studies are used as foundational research for the development of the automated kiosk. The first study evaluates human-computer interaction and how SPECIES agents can change perceptions of information systems by varying appearance and demeanor. Instantiations that had the agents embodied as males were perceived as more powerful, while female embodied agents were perceived as more likable. Similarly, smiling agents were perceived as more likable than neutral demeanor agents. The second study demonstrated that a single sensor measuring vocal pitch provides SPECIES with environmental awareness of human stress and deception. The final study ties the first two studies together and demonstrates an avatar-based kiosk that asks questions and measures the response using vocalic measurements.