Project Description
Research suggests that the success of refugees' integration may depend on the location to which they are assigned, as specific locations may be better suited to certain characteristics of refugees (Bansak et al. (2018)). To this end, there is a growing interest in developing and deploying algorithmic refugee-location matching tools.
The most prominent tool - GeoMatch - draws on supervised machine learning to predict refugee integration outcomes in potential assignment locations. Further, the tool applies an optimal matching approach to strategically assign refugees to locations where the probability of a desired integration outcome is maximised. The tool has been piloted by the Swiss State Secretariat for Migration (SEM) in Switzerland since 2020, by Global Refuge in the U.S. since 2023, and by the Central Agency for the Reception of Asylum Seekers (COA) in the Netherlands since 2024. Further, the tool was tested by the Directorate of Integration and Diversity (IMDi) in Norway in 2022.
Although GeoMatch and similar tools (AnnieTM Moore, Match’In, Re:Match, RUTH) are designed to support rather than replace human decision-makers, they raise important questions about their reliability. In our research, we critically evaluate and compare existing matching tools with a focus on fairness. By assessing these tools, we aim to provide a deeper understanding of how algorithmic matching tools differ and which key components must be considered to ensure fair and reliable algorithmic decision-making in dynamic contexts.
In our empirical research, we evaluate GeoMatch for the first time in the German context by drawing on data from the German Socio-Economic Panel (SOEP).
Contact Persons
Publications
- Strasser Ceballos, C. and Kern, C. (2025). Location matching on shaky grounds: Re-evaluating algorithms for refugee allocation. Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (FAccT '25). https://doi.org/10.1145/3715275.3732149
- Strasser Ceballos, C., Kern, C., Kappenberger, J., Gerdon F., Szafran, D., Rupp, F., Bach, R. (2025). A Simulation Framework for Studying the Social Impacts of Algorithm-Based Refugee Matching. Proceedings of Fourth European Workshop on Algorithmic Fairness. https://proceedings.mlr.press/v294/kern25a.html
- Strasser Ceballos, C., Novotny, M., Kern, C. (2025). Re-evaluating the role of refugee integration factors for building more equitable allocation algorithms. Proceedings of Fourth European Workshop on Algorithmic Fairness. https://proceedings.mlr.press/v294/ceballos25a.html
- Strasser Ceballos, C. and Kern, C. (2024). Deciding the Future of Refugees: Rolling the Dice or Algorithmic Location Assignment? Proceedings of the 3rd European Workshop on Algorithmic Fairness (EWAF’24). https://ceur-ws.org/Vol-3908/