Airport artificial intelligence can detect deception: or am i lying?
Since the 9/11 terrorist attacks, research has enveloped numerous areas within the psychological sciences as a means to increase the ability to spot potential threats. While airports took to heightened security protocols, many academics looked deeper into ways of detecting deception within international airport settings. Various verbal and nonverbal systems were intensely scrutinised under the empirical magnifying glass with the aim of creating security environments that are better able to detect potential threats. However, in 2018, a €4.5 m grant from the European Union’s Horizon 2020 research and innovation programme, number 700,626, was awarded to further in vivo test the use of computational methods to detect deception from facial cues. The system is deemed a noninvasive psychological profiling system and stems from that of a system called ‘Silent Talker’ (Rothwell et al. in Appl Cognit Psychol 20(6):757–777, 2006). The ‘iBorderCtrl’ AI system uses a variety of ‘at home’ pre-registration systems and real time ‘at the airport’ automatic deception detection systems. Some of the critical methods used in automated deception detection are that of micro-expressions. In this opinion article, we argue that considering the state of the psychological sciences current understanding of micro-expressions and their associations with deception, such in vivo testing is naïve and misinformed. We consider the lack of empirical research that supports the use of micro-expressions in the detection of deception and question the current understanding of the validity of specific cues to deception. With such unclear definitive and reliable cues to deception, we question the validity of using artificial intelligence that includes cues to deception, which have no current empirical support.
KeywordsLie detection Airport security Artificial intelligence Machine learning iBorderCtrl
- ActuIA. 2019. iBorderCtrl : A dangerous misunderstanding of what AI really is—ActuIA. https://www.actuia.com/english/iborderctrl-a-dangerous-misunderstanding-of-what-ai-really-is/. Accessed 5 Mar 2019.
- Adelson, R. 2004. Detecting deception. Monitor on Psychology 35: 7.Google Scholar
- Barrett Feldman, L. 2014. Opinion|what faces can’t tell us. https://www.nytimes.com/2014/03/02/opinion/sunday/what-faces-cant-tell-us.html. Accessed 8 Mar 2019.
- Barrett Feldman, L. 2017a. How emotions are made: The secret life of the brain. Boston, MA: Houghton Mifflin Harcourt.Google Scholar
- Barrett Feldman, L. 2017b. Why our emotions are cultural—not built in at birth. https://www.theguardian.com/lifeandstyle/2017/mar/26/why-our-emotions-are-cultural-not-hardwired-at-birth. Accessed 8 Mar 2019.
- Bernal, N. 2018. AI lie detectors to be tested by the EU at border points. The Telegraph. https://www.telegraph.co.uk/technology/2018/11/01/ai-lie-detectors-tested-eu-border-points/.
- Best, S. 2017. The robot that knows when you’re lying: Scientists create an AI that can detect deception in the courtroom (and it’s already “significantly better” than humans). http://www.dailymail.co.uk/sciencetech/article-5197747/AI-detects-expressions-tell-people-lie-court.html. Accessed 12 Aug 2018.
- Bringsjord, S., and B. Schimanski. 2003. What is artificial intelligence? Psychometric AI as an answer. In IJCAI International Joint Conference on Artificial Intelligence (pp. 887–893). http://www.cyc.com.
- Burgoon, J.K., D.B. Buller, A.S. Ebesu, C.H. White, and P.A. Rockwell. 1996. Testing interpersonal deception theory: Effects of suspicion on communication behaviors and perceptions. Communication Theory 6 (3): 243–267. https://doi.org/10.1111/j.1468-2885.1996.tb00128.x.CrossRefGoogle Scholar
- Colwell, K., C.K. Hiscock-Anisman, A. Memon, L. Taylor, and J. Prewett. 2007. Assessment criteria indicative of deception (ACID): An integrated system of investigative interviewing and detecting deception. Journal of Investigative Psychology and Offender Profiling 4 (3): 167–180. https://doi.org/10.1002/jip.73.CrossRefGoogle Scholar
- EDRi. 2019. Greece: Clarifications sought on human rights impacts of iBorderCtrl–EDRi. https://edri.org/greece-clarifications-sought-on-human-rights-impacts-of-iborderctrl/. Accessed 8 Mar 2019.
- Ekman, P. 2019. Facial action coding system|micro expressions. https://www.paulekman.com/product-category/facs/. . Accessed 8 Mar 2019.
- Freeborn, D. 2006. From old English to standard English : a course book in language variation across time. Studies in English language series. http://hotfile.com/dl/83728759/def5fc0/From.old.english.to.standard.english.rar.
- Gallagher, R., and L. Jona. 2019. We tested Europe’s new digital lie detector. It failed. https://theintercept.com/2019/07/26/europe-border-control-ai-lie-detector/. Accessed 27 Aug 2019.
- Ghosh, P. 2019. AAAS: Machine learning “causing science crisis”—BBC News. https://www.bbc.co.uk/news/science-environment-47267081. Accessed 5 Mar 2019.
- Granhag, P.A., and M. Hartwig. 2015. The strategic use of evidence technique: A conceptual overview. In Detecting deception: Current challenges and cognitive approaches. (pp. 231–251). Hoboken: Wiley-Blackwell.Google Scholar
- Hadid, A. 2014. Face biometrics under spoofing attacks: Vulnerabilities, countermeasures, open issues, and research directions. In IEEE computer society conference on computer vision and pattern recognition workshops (pp. 113–118). https://doi.org/10.1109/CVPRW.2014.22.
- Henig, R.B. 2006. Looking for the lie. https://www.nytimes.com/2006/02/05/magazine/looking-for-the-lie.html?_r=1. Accessed 12 Aug 2018.
- Homo Digitalis. 2019. Homo Digitalis Reporting to the Hellenic Parliament on the use of the IBORDERCTRL system at the Greek border. https://www.homodigitalis.gr/posts/2771. Accessed 5 Mar 2019.
- Honts, C.R., M. Hartwig, S.M. Kleinman, and C.A. Meissner. 2009. Credibility assessment at portals: Portals committee report. Final Report of the Portals Committee to the Defense Academy for Credibility Assessment. Google Scholar
- iBorderCtrl. 2019. Technical framework. https://www.iborderctrl.eu/Technical-Framework. Accessed 27 Feb 2019.
- Jupe, L.M., and M. Hartwig. 2019. Deception, anxiety and folk beliefs: An examination of the asymmetrical anxiety heuristic. Manuscript in Preperation.Google Scholar
- Kleinberg, B., A. Arntz, and B. Verschuere. 2019. Being accurate about verbal credibility assessment. https://doi.org/10.31234/OSF.IO/H6PXT.
- Kohli, N., D. Yadav, M. Vatsa, R. Singh, and A. Noore. 2016. Detecting medley of iris spoofing attacks using DESIST. In 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems, BTAS 2016. https://doi.org/10.1109/BTAS.2016.7791168.
- Lovell-Badge, R. 2013. Nine out of ten statistics are taken out of context|Understanding Animal Research|Understanding Animal Research. http://www.understandinganimalresearch.org.uk/news/communications-media/nine-out-of-ten-statistics-are-taken-out-of-context/. Accessed 5 Mar 2019.
- Marcel, S., M. Nixon, and S. Li. 2014. Handbook of Biometric Anti-Spoofing. http://link.springer.com/content/pdf/10.1007/978-1-4471-6524-8.pdf.
- McGrath, C. 2018, November 2. Lie detector scheme to boost fight against “terror threats” trialled at EU borders. The Express. https://www.express.co.uk/news/world/1040150/eu-news-lie-detector-scheme-fight-terror-threats-trialled-borders-hungary.
- Nahari, G. (2018). The applicability of the verifiability approach to the real world. In P. R. J (Ed.), Detecting concealed information and deception: recent developments (pp. 329–349). https://doi.org/10.1016/B978-0-12-812729-2.00014-8.CrossRefGoogle Scholar
- National Crime Agency. 2017. Identity crime. http://www.nationalcrimeagency.gov.uk/crime-threats/identity-crime. Accessed 14 Dec 2017.
- Ortony, A., and T.J. Turner. (1990). What’s basic about basic emotions? Psychological Review (Vol. 97). Retrieved from https://pdfs.semanticscholar.org/df84/be52a5c0a51db7e9545a0bdd2ab3c389cc3b.pdf.
- Poole, D.L., A.K. Mackworth, and R. Goebel. 1998. Computational intelligence: A logical approach (1st ed.). New York.Google Scholar
- Rozin, P., L. Lowery, and R. Ebert. 1994. Varieties of disgust faces and the structure of disgust. Journal of Personality and Social Psychology (Vol. 66). Retrieved from https://pdfs.semanticscholar.org/123b/28a4b062a73daa4abef15e91e23b49382bf4.pdf.
- Twyman, N.W., J.G. Proudfoot, R.M. Schuetzler, A.C. Elkins, and D.C. Derrick. 2015. Robustness of multiple indicators in automated screening systems for deception detection. Journal of Management Information Systems 32 (4): 215–245. https://doi.org/10.1080/07421222.2015.1138569.CrossRefGoogle Scholar
- Vrij, A. 2000. Detecting lies and deciet: The psychology of lying and the implications for professional practice. Chichester: Wiley.Google Scholar
- Vrij, A., and G. Ganis. 2014. Theories in deception and lie detection. Credibility Assessment: Scientific Research and Applications. https://doi.org/10.1016/B978-0-12-394433-7.00007-5.CrossRefGoogle Scholar
- Vrij, A., S. Mann, S. Leal, Z. Vernham, and M. Vaughan. 2016b. Train the trainers: A first step towards a science-based cognitive lie detection training workshop delivered by a practitioner. Journal of Investigative Psychology and Offender Profiling 13 (2): 110–130. https://doi.org/10.1002/jip.1443.CrossRefGoogle Scholar
- Wang, W.S.Y. 1979. Language change a lexical perspective. Annual Review of Anthropology 8 (1): 353–371. https://doi.org/10.1146/annurev.an.08.100179.002033.CrossRefGoogle Scholar
- Widrow, B., and M. Hoff. 1960. Adaptive switching circuits. https://apps.dtic.mil/dtic/tr/fulltext/u2/241531.pdf.
- Woollacott, E. 2017, August 1. Better drugs, faster: The potential of AI-powered humans. BBC News. https://www.bbc.co.uk/news/business-40708043.
- Wu, Z., B. Singh, L.S. Davis, and V.S. Subrahmanian. 2017. Deception detection in videos. http://arxiv.org/abs/1712.04415.