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MIGRATION FORESIGHT: Artificial intelligence and machine learning applications in migration research and practice

View of immigration control at Changi International Airport in Singapore

Yousef Khalifa Aleghfeli (Project Manager), Vaidehi Atodaria (Researcher), Sarah Hoyos-Hoyos (Methodological Advisor)

This project aims to examine and explore how artificial intelligence (AI) and machine learning (ML) are transforming how we observe and understand international immigration. This involves building and testing predictive models that forecast cross-border migration flows to support migration practice, and critically reviewing the application of AI- and ML-based computational methods in migration research. Outputs from this project are intended to inform a future research agenda on how artificial intelligence and machine learning can support rights-based migration governance.

  1. How are artificial intelligence and machine learning transforming migration research and practice?
  2. How can AI- and ML-based computational models help identify key push factors likely to impact mobility trends?
  3. How are AI- and ML-based computational methods transforming the way we do migration research?

The project responds to two challenges facing migration practice and research. First, governments and international organizations face difficulties in anticipating and responding to evolving migration trends. Changing migration dynamics, driven by factors such as geopolitical crises, environmental hazards, and structural challenges, require real-time monitoring and analysis to support effective humanitarian preparedness and resource allocation. Second, while AI and ML applications are proliferating across migration research, we lack a systematic understanding of how these technologies are deployed and their implications. These computational methods enable researchers to analyze unprecedented volumes of data, yet alongside these capabilities are concerns about privacy, algorithmic bias, and the potential for such systems to perpetuate structural inequalities. By developing AI- and ML-based computational models to support migration practice and systematically mapping the application of AI- and ML-based computational methods in migration research, this project will develop insights into how AI and ML are transforming how we observe and understand international migration, with implications for pursuing a more rights-based governance.

The project uses a concurrent two-phase methodology:

  • Phase 1 develops and evaluates a predictive gradient boosting model to identify key push factors (geopolitical and environmental) driving cross-border migration from UN-designated least-developed countries and forecast future migration flows to aid humanitarian response and strategic planning.
  • Phase 2 maps the computational turn in migration research through critically reviewing the application of artificial intelligence and machine learning in migration research in order to identify patterns and trends around which computational methods are used for which types of research questions.

Aleghfeli, Y. K. [chair], & Hoyos-Hoyos, S. [chair] (2025, May 26). Migration Foresight: Lessons from researching emerging migration trends [Symposium]. Annual Conference of the Canadian Association for Refugee and Forced Migration Studies, Toronto, ON, Canada.

Dec. 2025

CERC Migration

Partners

Global Data Institute, International Organization for Migration (IOM)

cross-border migration; internal displacement; data science