1 citations · 3 across the 4 of their papers we have counts for
4 papers
Matchings, Predictions and Counterfactual Harm in Refugee Resettlement Processes
Seungeon Lee, Nina Corvelo Benz, Suhas Thejaswi +1
Resettlement agencies have started to adopt data-driven algorithmic matching to match refugees to locations using employment rate as a measure of utility. Given a pool of refugees,…
GeoSEE: Regional Socio-Economic Estimation With a Large Language Model
Sungwon Han, Donghyun Ahn, Seungeon Lee +5
Moving beyond traditional surveys, combining heterogeneous data sources with AI-driven inference models brings new opportunities to measure socio-economic conditions, such as pover…
Fine-Grained Socioeconomic Prediction from Satellite Images with Distributional Adjustment
Donghyun Ahn, Minhyuk Song, Seungeon Lee +5
While measuring socioeconomic indicators is critical for local governments to make informed policy decisions, such measurements are often unavailable at fine-grained levels like mu…
DualFair: Fair Representation Learning at Both Group and Individual Levels via Contrastive Self-supervision
Sungwon Han, Seungeon Lee, Fangzhao Wu +5
Algorithmic fairness has become an important machine learning problem, especially for mission-critical Web applications. This work presents a self-supervised model, called DualFair…