On May 13, 2025, the Digital Data Design (D^3) Institute at Harvard held a university-wide Generative AI Symposium in partnership with the Office of the Vice Provost for Research, the Office of the Vice Provost for Advances in Learning, the Faculty of Arts and Sciences, the Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University Information Technology and Harvard Library. This half-day event for Harvard faculty, students, and staff focused on the impact of AI on research, teaching, operations, and innovative applications across professional schools and areas of practice.
In her session AI Recommendations for Refugee Resettlement & Using Data to Optimize Resource Allocation, Assistant Professor of Business Administration and D^3 Associate Elisabeth Paulson discussed the refugee relocation crisis, one of humanity’s most pressing challenges. Despite there being over 30 million people worldwide who need resettlement, approaches to refugee placement have mostly relied on manual processes and limited data, resulting in suboptimal outcomes for refugees and host communities. Paulson’s research and talk focus on how AI and machine learning can be utilized to model and optimize placement decisions, helping to improve this critical humanitarian process.
Key Insight: The Challenge of Successful Refugee Placement
“[O]ver half of the refugees that are resettled to the US do not find employment within 90 days, at which point their benefits are phased out.”
Elisabeth Paulson
In her presentation, Paulson highlighted that some locations have employment rates of around 5%, while others are above 40%. Specific locations have capacity limits, so simply relocating everyone to locations with higher employment rates is not possible, nor does it consider successful cases in all areas. The overall low employment rate and stark disparity in location rates underscores the critical importance of initial placement decisions. Paulson’s research aims to improve the placement decision process with AI and machine learning.
Key Insight: Optimizing the Assignment Problem
“[I]f we can predict these match qualities or these likelihoods of finding employment, then we can use optimization to find the optimal assignment of people to places.”
Elisabeth Paulson
A range of factors, such as gender and language proficiency, can affect whether a refugee will be successful in finding employment, but the importance and predictability of these factors differs across placement location, and the characteristics of refugee populations and host communities are dynamic and constantly in flux. Additionally, resettlement officers are forced to make placements one at a time (sequentially) without knowledge about the characteristics of future refugees. Paulson explained how AI and machine learning can help on both fronts by discovering synergies between people and successful employment locations, and using advanced mathematical modeling to balance sequential decision-making with long-term scenario probabilities. Using these methods, Paulson reported that US employment rates can increase by about six percentage points, which means thousands more who have been successfully relocated.
Key Insight: AI in Action through GeoMatch
“[A]ll of these ideas and tools that I just talked about are all incorporated into a software tool called GeoMatch.”
Elisabeth Paulson
The practical application of this research has culminated in the development of GeoMatch, a tool housed at the Stanford Immigration Policy Lab with pilots running in the US and Switzerland. GeoMatch streamlines, improves, and speeds up the decision-making process, taking just minutes compared to hours when done manually. The tool also maintains human oversight, allowing relocation officers to modify and overrule recommendations. Paulson hopes that technology and machine learning behind GeoMatch will prove useful in other regions around the world as well.
Why This Matters
For business leaders and executives, the application of AI in refugee resettlement offers valuable insights into the broader potential of AI for complex resource allocation challenges. The methodology of personalized matching and strategic forecasting offers parallels with customer segmentation, human capital allocation, and market entry strategies. It also serves as a blueprint for implementing AI solutions that deliver both operational efficiency and strategic advantage, which are particularly relevant as organizations navigate increasingly complex global markets while managing constrained resources and uncertain environments.
Meet the Speaker
Elisabeth Paulson is an Assistant Professor of Business Administration in the Technology and Operations Management Unit at Harvard Business School. Her research is in the area of operations for social good. In particular, she designs analytical methods and algorithms for allocating scarce resources efficiently and fairly to improve social outcomes. Much of her work draws on tools from optimization, machine learning, mathematical modeling, and statistics. She received her PhD in Operations Research from MIT.