Deception Detection in Online Automated Job Interviews
This research-in-progress paper presents a conceptual system for automated deception detection in online interviewing. The design proposes video recordings of responses to predefined, structured interview question sets variously selected based on the desired behavioral metric of interest, such as competence, social skills, or in this case, veracity. Raw behavioral data extracted from video responses is refined to produce indicators of behavioral metrics. A prototype implementation of the design was built and tested experimentally using a job interview scenario. Results of the experimental analysis provide evidence of the potential of the concept.
Twyman, N. W., Pentland, S. J., & Spitzley, L. (2018). Deception Detection in Online Automated Job Interviews. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10923 LNCS, pp. 206-216.
The definitive version is available at https://doi.org/10.1007/978-3-319-91716-0_16
5th International Conference on HCI in Business, Government, and Organizations, HCIBGO 2018 (2018: Jul. 15-20, Las Vegas, NV)
Business and Information Technology
Keywords and Phrases
Automated interviewing; Behavioral assessment; Deception detection; Human risk assessment; Virtual agent-based interviewing
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2018 Springer Verlag, All rights reserved.