Augmented borders: Big Data and the ethics of immigration control

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 scientific and research fields. 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 reflection 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 profiling enable the identification 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.