Algorithmic-driven or automated decision-making models (ADM) and programs are increasingly used by public administrations. To make sense of their function and decide whether or how to use them in migration of refugee policy will require consideration of the specific context in which they are being employed. This guide examines three concrete use cases at core nodes of migration policy in which automated decision-making is already either being developed or tested: visa application processes, placement matching to improve integration outcomes, and forecasting models to assist for planning and preparedness related to human mobility or displacement.
All cases raise the same categories of questions: from the data employed, to the motivation behind using a given system, to the action triggered by models. The nuances of each case demonstrate why it is crucial to understand these systems within a bigger socio-technological context and provide categories and questions that can help policymakers understand the most important implications of any new system, including both technical consideration as well as contextual questions.