Logo image
Airport artificial intelligence can detect deception: Or am i lying?
Journal article   Peer reviewed

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

Louise Marie Jupe and David Keatley
Security Journal, Vol.33(4), pp.622-635
2020

Abstract

Crime and Society Criminology and Criminal Justice General Original Article Social Sciences
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.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#16 Peace, Justice and Strong Institutions

Source: InCites

Metrics

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types
Domestic collaboration
International collaboration
Citation topics
1 Clinical & Life Sciences
1.7 Neuroscanning
1.7.2100 Deception Detection
Web Of Science research areas
Criminology & Penology
ESI research areas
Social Sciences, general
Logo image