One effective way for organizations to capture expert knowledge and experience is to encapsulate it within an expert system (ES) and make that system available to others. While ES users have access to the system's knowledge, they shoulder the difficult task of appropriately incorporating the ES recommendations into the decision-making process.One proposed application of an ES is in the realm of deception detection. Humans are inherently poor at recognizing deception when it occurs and their confidence in their judgments is poorly calibrated to their performance. An ES has the potential to significantly improve deception detection; however, joining an ES and a human decision maker creates many important questions that must be addressed before such a system will be useful in a field environment. These questions concern changes in decision outcomes, decision processes, and the decision maker that result from ES use.To examine these questions, a prototype system was created that implements new and unobtrusive methods of deception detection. Kinesic analysis examines the body movement of a potential deceiver and linguistic analysis reviews the structure of utterances from a potential deceiver. This prototype, complete with explanations, was utilized in two experiments that examined the effects of access to the prototype, accuracy level of the prototype, user training in deception detection, and novice or professional lie-catcher status of the users.Use of the prototype system was found to significantly improve professional and novice accuracy rates and confidence alignment. Training was found to have no effect on novice accuracy rates. Accuracy level of the prototype significantly elevated accuracy rates and confidence alignment among novices; however, this improvement was imperceptible to the novices. Novices using the prototype performed on a level equivalent to professionals using the prototype. Neither professional nor novice users of the prototype exceeded the performance of the prototype system alone. Implications of these findings include emphasizing the development of computer-based tools to detect deception and defining a new role for human users of such tools.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/193557 |
| Date | January 2007 |
| Creators | Jensen, Matthew Lynn |
| Contributors | Nunamaker, Jay F., Burgoon, Judee K., Nunamaker, Jay F., Burgoon, Judee K., Tanniru, Mohan, Rapoport, Amnon |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | text, Electronic Dissertation |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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