Making up the predictable border: How bureaucracies legitimate data science techniques

Abstract

This article examines how claims to predictable borders via data science techniques are crafted in bureaucratic institutions. Through a case study of testing algorithmic systems at a transnational agency, we examine how humanitarian organizations reconcile the risks of predictive technologies with the benefits they claim to receive. Drawing on a content analysis of policy documents and interviews with humanitarian technologists, we identify three organizational strategies to justify working toward predictability: constantly seeking novel variables and data, maintaining ambiguity, and shifting models to adapt to changing circumstances. These strategies, we argue, sustain the claim that a predictable border is possible even when the technical reality of machine learning models does not live up to bureaucratic imaginaries. The so-called success of a predictable border does not solely derive from its technical capacity to estimate human mobility accurately but from creating a semblance of a predictable border inside an organization.