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Airport artificial intelligence can detect deception: or am I lying?

Research output: Contribution to journalArticle

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.
Original languageEnglish
JournalSecurity Journal
Early online date24 Sep 2019
DOIs
Publication statusEarly online - 24 Sep 2019

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  • airport artificial intelligence

    Rights statement: This is a post-peer-review, pre-copyedit version of an article published in the Security Journal. TThe final authenticated version is available online at: https://doi.org/10.1057/s41284-019-00204-7.

    Accepted author manuscript (Post-print), 431 KB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 24/09/20

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