The politics of deceptive borders: ‘biomarkers of deceit’ and the case of iBorderCtrl

Abstract

This paper critically examines a recently developed proposal for a border control system called iBorderCtrl, designed to detect deception based on facial recognition technology and the measurement of micro-expressions, termed 'biomarkers of deceit'. Funded under the European Commission's Horizon 2020 programme, the system is analysed in relation to the wider political economy of 'emotional AI' and the history of deception detection technologies. We then move on to interrogate the design of iBorderCtrl using publicly available documents and assess the assumptions and scientific validation underpinning the project design. Finally, drawing on a Bayesian analysis we outline statistical fallacies in the foundational premise of mass screening and argue that it is very unlikely that the model that iBorderCtrl provides for deception detection would work in practice. By interrogating actual systems in this way, we argue that we can begin to question the very premise of the development of datadriven systems, and emotional AI and deception detection in particular, pushing back on the assumption that these systems are fulfilling the tasks they claim to be attending to and instead ask what function such projects carry out in the creation of subjects and management of populations. This function is not merely technical but, rather, we argue, distinctly political and forms part of a mode of governance increasingly shaping life opportunities and fundamental rights.

Understanding surveillance capitalism from the viewpoint of migration

“…For example, the border control and immigration budget of the United States has increased by 6,000 per cent since 1980, with US$223 million of the 2019 budget set aside to Homeland Security for the development of border security technologies. The EU, on the contrary, has allocated €34.9 billion to this field for the 2021–27 period, up from €13 billion for the 2014–20 period (Achiume, 2020; Daniels, 2018; Feldstein, 2019; Sánchez‐Monedero & Dencik, 2020).…”

“…For example, a lie detector algorithm was developed by QinetiQ, a British defence technology company and funded by the UK Engineering and Physical Sciences Research Council. Another project is the iBorderCtrl conducted by Manchester Metropolitan University researchers, which benefited from a €4.5 million grant provided by the European Research Council (Sánchez‐Monedero & Dencik, 2020). This project made use of affect recognition technologies and was tested in Greece, Hungary and Latvia (Feldstein, 2019), while another highly criticized pilot study is the UK Border Agency's Human Provenance Pilot (HPP) Project, which aims to detect the nationality of asylum seekers through a DNA and isotope analysis (Privacy International & No Tech for Tyrants, 2020).…”

Biometric identity systems in law enforcement and the politics of (voice) recognition: The case of SiiP

“…Furthermore, the reductionist categories of binary labels inferred by soft biometrics rely on criteria based on already available data that is unlikely to match the cultural and social diversity of the general population. Such uses of soft biometrics have been shown to lack scientific validation and often rely on deeply contested assumptions about physiological attributes and the relationship between people and data (Sánchez-Monedero and Dencik, 2020). Pre-selecting the digital representations of some voices collected from data infrastructures to inform the labelling of criminals or terrorists invokes a particular regime of recognition, in which institutional priorities shape what dispersed groups of individuals are recognized and algorithmically processed by law enforcement on the basis of sharing certain voice characteristics.…”

Emotional AI, soft biometrics and the surveillance of emotional life: An unusual consensus on privacy

“…Application of emotion tracking has progressed from in-house research facilities (such as those used in neuromarketing to detect responses to adverts) to online and physical contexts. Including emojis (Davies, 2016;Stark and Crawford, 2015), wearables (Lupton, 2016;Neff and Nafus, 2016;Picard, 1997), human-robot interaction (Bryson, 2018), education (Williamson, 2017), retail (Turow, 2017), employee behaviour (Davies, 2015;Grandey et al, 2013) and border control (Sa´nchez-Monedero and Dencik, 2019), the broader business strategy for emotional AI companies is ubiquitous usage of automated emotion detection in all personal, commercial and public contexts. To suggest a broad rule, if there is any form of value in understanding emotion in a given context, emotional AI has scope to be employed.…”