I. Machine Learning for migration research: innovations, challenges, and practical implications

Jun 16, 2025 | 4:30 PM - 6:00 PM

Auditorium B1, Niagara:B0E15

Description

Over the last decade, the transformative potential of machine learning (ML) has been increasingly recognized in migration research, not only in terms of its ability to enhance the accuracy and timeliness of migration statistics and forecasts, but also to uncover insights at the scales that traditional methods are incapable to do so. Meanwhile, the uptake of ML has also raised myriad new questions and challenges concerning, for example, data and algorithmic biases, ethical considerations, and the implications for policymaking as well as for the wellbeing of migrant populations. This panel session will present numerous case studies to demonstrate the potential of ML for migration research. While highlighting the methodological innovations, the panelists will also discuss the limitations as well as the societal impact of these state-of-the-art approaches. Distinguished panelists at the session include the founding partners of CLIMB (an international consortium pioneering in leveraging machine learning to study climate-induced migration https://climbproject.org/). In addition, the Editor-in-Chief of International Migration Review will present an overview of how migration research and models have evolved over the last decade. The panelists will bring a wealth of multidisciplinary expertise from social science, statistics, demography, agricultural science, and remote sensing. Moderator: Haodong Qi, Researcher, Malmö University Speakers: - Holly Reed, Professor & Editor-in-Chief of International Migration Review, City University New York - Laure Tall, Director, Initiative Prospective Agricole et Rurale, Data Population Alliance, Senegal - Stefano Iacus, Director of Data Science and Product Research, Institute for Quantitative Social Science, Harvard University - Tuba Bircan, Professor, Vrije University Brussels - Stefan Lang, Professor, Professor, Paris Lodron University of Salzburg