Abstract
Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in-depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these tools’ scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short- or longer-term migration patterns, or to assess and estimate migration uncertainties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance.
Highlights
This study analyses how existing supranational predictive IT tools address these issues to achieve effective migration governance in the EU. This two‐fold inquiry first asks: What are the main predictive migration tools and what is their scope? Here we focus on identifying the variables and data sources used to cre‐ ate the tool models, as well as their underlying objec‐ tives and rationale, exploring how this relates to the gov‐ ernance of migratory flows to date in their respective target countries
The interviewees were selected according to their expertise, using the snowball sampling method, and included: the three developers of the forecasting tools reviewed in this article, an Internal Displacement Monitoring Centre (IDMC) representative regarding the artificial intelli‐ gence (AI) tool Internal Displacement Event Tagging and Clustering Tool (IDETECT), and the founding devel‐ oper of the Global Database of Events, Language, and Tone (GDELT) project
The analysis demonstrates the chal‐ lenges in providing for effective interaction and feedback among tool developers and end‐users, and how each of these tools has a different scope, data sources, models, and validation mechanisms, according to their goals
Summary
The interviewees were selected according to their expertise, using the snowball sampling method, and included: the three developers of the forecasting tools reviewed in this article, an IDMC representative regarding the AI tool IDETECT, and the founding devel‐ oper of the Global Database of Events, Language, and Tone (GDELT) project (which monitors the world’s broad‐ cast, print, and web news in over 100 languages). These interviews first explored the scope of the different tools, and secondly inquired as to what extent the existing pre‐. All the data collected via the document analy‐ sis as well as the interviews were systematically entered into an Excel spreadsheet and organised by the different categories according to the study’s two key objectives, as Table 1 illustrates
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