Virtual Borders: International Law and the Elusive Inequalities of Algorithmic Association

Abstract

The use of algorithmic tools by international public authorities is changing how norms are made and enacted. This seismic shift in global governance has important distributive consequences: the digital turn not only empowers specific corporate actors and forms of expertise but also entails new modes of social sorting based on the placement of people in patterns of data. This article focuses on the emergent inequalities that machine learning and data analytics thereby import in the domain of global governance. In line with the symposium's theme, I thereby frame the importance of computational decision-making processes from a distributional, and not a procedural, perspective - from a perspective of inequality and not privacy, data protection or transparency. The empirical site for the assessment of these emergent inequalities is the 'virtual border'. By focusing on the technological tools of data extraction and algorithmic risk assessment that are reshaping practices of border control, the article makes a dual contribution: it reveals the social hierarchies engendered by these data-driven forms of grouping and grading - captured in the novel concept of 'associative inequality' - and highlights the difficulty of registering or counteracting this mode of subject-making in existing legal terms. This intervention both traces the particular distributive effects of data-driven governance and signals the challenges it poses to the prospects and emancipatory promises of collectivity, solidarity and equality entertained in modernist ideals of international law. In resisting the logic of algorithmic governance, I suggest, we should strive not for transparency but for opacity, not inclusion but incomparability, not privacy but open-ended and defiant commonality.