  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, we situate our analysis in 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
data-driven 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.
