Augmented borders: Big Data and the ethics of immigration control
| Published date | 09 March 2015 |
| Date | 09 March 2015 |
| DOI | https://doi.org/10.1108/JICES-01-2014-0005 |
| Pages | 58-78 |
| Author | Btihaj Ajana |
| Subject Matter | Information & knowledge management,Information management & governance |
Augmented borders: Big Data
and the ethics of
immigration control
Btihaj Ajana
CMCI and Digital Humanities, King’s College London, London, UK
Abstract
Purpose – Investments in the technologies of borders and their securitisation continue to be a focal
point for many governments across the globe. This paper is concerned with a particular example of such
technologies, namely, “Big Data” analytics. In the past two years, the technology of Big Data has gained
a remarkable popularity within a variety of sectors, ranging from business and government to scientic
and research elds. While Big Data techniques are often extolled as the next frontier for innovation and
productivity, they are also raising many ethical and political issues. The aim of this paper is to consider
some of these issues and provide a critical reection on the implications of using Big Data for the
governance of borders.
Design/methodology/approach – The author draws on the example of the new Big Data solution
recently developed by IBM for the Australian Customs and Border Protection Service. The system,
which relies on data collected from Passenger Name Records, aims to facilitate and automate
mechanisms of proling enable the identication of “high-risk” travellers. It is argued that the use of
such Big Data techniques risks augmenting the function and intensity of borders.
Findings – The main concerns addressed here revolve around three key elements, namely, the
problem of categorisation, the projective and predictive nature of Big Data techniques and their
approach to the future and the implications of Big Data on understandings and practices of identity.
Originality/value – By exploring these issues, the paper aims to contribute to the debates on the
impact of information and communications technology-based surveillance in border management.
Keywords Ethics, Surveillance, Immigrants, Human rights, Identity, Dataveillance
Paper type Research paper
Borders and their securitisation continue to be a major concern for governments across
the world. Advanced information systems and technologies are increasingly being
looked up to as a solution for managing the ow of people and things. Recently, there has
been a growing interest in “Big Data” analytics and its potential to enhance the means
by which vast data can be effectively analysed and transformed into more ne grained
knowledge to enable faster and more advanced decision making processes vis-a`-vis
access, or denial of it, across international borders. In Australia, for instance, the
Department of Immigration and Citizenship (DIAC) has developed the Border Risk
Identication System (BRIS) which relies on Big Data tools to construct patterns and
correlations for improving border management and targeting so-called “risky
travellers” (The Australian Customs and Border Protection Service (ACBPS) (2013), Big
Data Strategy, 2013). While in Europe, programmes such as European border
The author wishes to thank Ofer Engel and Michael Takeo Magruder for the inspiring
conversations on Big Data.
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1477-996X.htm
JICES
13,1
58
Received 20 March 2014
Revised 20 March 2014
Accepted 7 October 2014
Journalof Information,
Communicationand Ethics in
Society
Vol.13 No. 1, 2015
pp.58-78
©Emerald Group Publishing Limited
1477-996X
DOI 10.1108/JICES-01-2014-0005
surveillance system and Frontex are examples of information and communications
technology (ICT)-mediated surveillance whereby Big Data techniques are increasingly
utilised for predicting, monitoring and controlling movements across European Union
(EU) borders. In addition, it is just a matter of time before other countries start adopting
Big Data for the governance of immigration. Despite this increasing interest in Big Data
within immigration policy, border management and beyond, there is a marked absence
of studies that directly deal with the wider impacts of Big Data on immigration politics
and governance, as the majority of available literature on Big Data tends to mainly focus
on their popularity and potential for value creation. As a response and by referring to the
example of Australia’s recently developed BRIS, this paper looks at the relation of Big
Data to borders and addresses some of the ethical implications of such techniques in the
management of immigration and movement. I begin with an examination of the concept
of Big Data itself followed by a reection on borders and their redenition by way of
opening up a discussion on the implications of Big Data vis-a`-vis immigration
governance. Three interrelated concerns are being examined throughout this paper.
First, I discuss the issue of “categorisation” and its far-reaching impacts that touch the
very question of the “human” itself. The second issue relates to “projection” and the
predictive nature of Big Data. I argue that the analytic techniques of Big Data encourage
a preemptive and parochial attitude towards the future, and enable the systematic
proling of people and the forming of various categorical assumptions about their
character and risk potential. The third issue concerns the question of “identity” and its
conceptualisation in Big Data. Here, I stress the importance of embodiment in
understanding what is at stake in the management and control of identity through Big
Data for the purpose of immigration and border management. In light of these concerns,
the paper advocates an embodied ethical approach to borders, one that can recognise the
corporeal conditions and material consequences of Big Data use, and leverage against
the security-driven and fear-based visions currently perpetuated by data industries and
governmental institutions alike.
The rise of Big Data
Recently, the buzzword of Big Data has invaded many spheres of production,
knowledge and expertise. From marketing and advertising to health care and
bioinformatics, various elds are currently exploring the possible benets and
challenges pertaining to the collection and usage of large data sets for different purposes
and contexts. Generally, Big Data are often dened as “data sets whose size is beyond
the ability of typical database software tools to capture, store, manage, and analyze”
(McKinsey Global Institute, 2011), requiring, as such, more enhanced technologies and
advanced analytic capabilities. The purpose of Big Data analytics is very much about
prediction and decision-making, focussing on “why events are happening, what will
happen next, and how to optimize the enterprise’s future actions” (Parnell in Field
Technologies Online, 2013). Big data[1] are aggregated from a variety of sources
including social media, web search histories, online transactions records, mobile
technologies and sensors that gather information about location and any other source
where digital traces are left behind knowingly or unknowingly. Some of these data
are actively volunteered by users and consumers on networking sites, for instance, while
others are collected through various means and technologies embedded within the
routine activities of everyday life. Given the rise of social networking and mobile
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Augmented
borders
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