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

Abstract

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 €4.5m grant from the European Union's Horizon 2020 research and innovation programme, number 700626, was awarded to further in vivo test the use of computational methods to detect deception from facial cues. The system is deemed a non-invasive psychological profiling system and stems from that of a system called 'Silent Talker' (Rothwell, Bandar, O'Shea, & McLean, 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 is that of microexpressions. 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.

Veracity judgement, not accuracy: Reconsidering the role of facial expressions, empathy, and emotion recognition training on deception detection

“…The current findings may be compounded by extraneous factors brought about by human detection such as perceptual or processing limitations and judgmental biases. However, such research must have strong theoretical and empirical foundations (Jupe & Keatley, 2020; Zloteanu, 2020).…”

The emergence of “truth machines”?: Artificial intelligence approaches to lie detection

“…As I have described in this article, many AI-related research and development efforts are in use today despite the fact that they are in the early stages of testing and evaluation (Bittle, 2020). Some expressions of skepticism about the value of AI-enhanced lie detection are also emerging in security studies and legal research as scientific support for its use is often spotty (Jupe & Keatley, 2019). Some commentators identify lie detection as "little more than a racket" (Stroud, 2019).…”

11 Artificial Intelligence, Migration and Mobility: Implications for Policy and Practice

“…41 Al Hamar et al, 2018Rawlings, 2019. 42 Al Hamar et al, 2018Huszti-Orbán and Ní Aoláin, 2020;Jupe and Keatley, 2019. 43 Fang and Biddle, 2020. 44 Ibid.…”