The datafication of borders and management of refugees in the context of Europe

This report provides an overview of the datafication of borders and the management ofrefugees within the context of the EU. It analyses different reports, papers and systems that arepart of the data processes confronted by refugees and asylum seekers. The report is focused onexisting systems used by the EU and the UNHCR, but also draws on further studies on the useof Big Data in the context of refugees

Data Justice

Data justice has emerged as a key framework for engaging with the intersection of datafication and society in a way that privileges an explicit concern with social justice. Engaging with justice concerns in the analysis of information and communication systems is not in itself new, but the concept of data justice has been used to denote a shift in understanding of what is at stake with datafication beyond digital rights. In this essay, we trace the lineage and outline some of the different traditions and approaches through which the concept is currently finding expression. We argue that in doing so, we are confronted with tensions that denote a politics of data justice both in terms of what is at stake with datafication and what might be suitable responses

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

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

How to (partially) evaluate automated decision systems

Depicting the social impact of automated decision systems requires multiple interdisciplinary entry-points. In this paper we focus on the actual data and algorithms that produce specific outputs for the purposes of decision-making. The aim of this report is to outline the range of prominent methods that are used for auditing algorithms in data-driven systems and to also consider some of their limitations

Justicia de datos

Data justice has emerged as a key framework for engaging with the intersection of datafication and society in a way that privileges an explicit concern with social justice. Engaging with justice concerns in the analysis of information and communication systems is not in itself new, but the concept of data justice has been used to denote a shift in understanding of what is at stake with datafication beyond digital rights. In this essay, we trace the lineage and outline some of the different traditions and approaches through which the concept is currently finding expression. We argue that in doing so, we are confronted with tensions that denote a politics of data justice both in terms of what is at stake with datafication and what might be suitable responses

ORCA: A Matlab/Octave toolbox for ordinal regression

Ordinal regression, also named ordinal classification, studies classification problems where there exist a natural order between class labels. This structured order of the labels is crucial in all steps of the learning process in order to take full advantage of the data. ORCA (Ordinal Regression and Classification Algorithms) is a Matlab/Octave framework that implements and integrates different ordinal classification algorithms and specifically designed performance metrics. The framework simplifies the task of experimental comparison to a great extent, allowing the user to: (i) describe experiments by simple configuration files; (ii) automatically run different data partitions; (iii) parallelize the executions; (iv) generate a variety of performance reports and (v) include new algorithms by using its intuitive interface. Source code, binaries, documentation, descriptions and links to data sets and tutorials (including examples of educational purpose) are available at https://github.com/ayrna/orca