Borderline decisions?: Lack of justification for automatic deception detection at EU borders

Daniel Minkin; Lou Therese Brandner

doi:10.14512/tatup.33.1.34

Authors

Daniel Minkin High-Performance Computing Center Stuttgart, University of Stuttgart, Stuttgart (DE) https://orcid.org/0009-0001-6853-416X hpcdmink@hlrs.de
Lou Therese Brandner International Center for Ethics in the Sciences and Humanities, University of Tübingen, Tübingen (DE) https://orcid.org/0000-0003-3827-0631 lou.brandner@uni-tuebingen.de

DOI:

https://doi.org/10.14512/tatup.33.1.34

Keywords:

automatic deception detection, machine learning, emotion recognition, border control, trust

Abstract

Between 2016 and 2019, the European Union funded the development and testing of a system called “iBorderCtrl”, which aims to help detect illegal migration. Part of iBorderCtrl is an automatic deception detection system (ADDS): Using artificial intelligence, ADDS is designed to calculate the probability of deception by analyzing subtle facial expressions to support the decision-making of border guards. This text explains the operating principle of ADDS and its theoretical foundations. Against this background, possible deficits in the justification of the use of this system are pointed out. Finally, based on empirical findings, potential societal ramifications of an unjustified use of ADDS are discussed.

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