The 2020 Data Science for Social Good summer program wrapped up this week, completing 8 weeks of intensive work on three projects.
Improving predictions for targeted human trafficking investigations in Brazil
In partnership with the Human Trafficking Data Lab at Stanford one team helped help the Brazilian Federal Labor Prosecution Office in targeting their investigations into firms involved in human trafficking. Specifically, they utilized data science tools to build the Intuition Engine – an ensemble predictive model combining regression models, spatial data science, natural language processing, deep learning and network analyses to better detect the risk of trafficking. They focused on the regression part of the Intuition Engine, constructing statistical models to identify the strongest predictors of human trafficking.
Building a network of land ownership in Kenya
Partnering with Code for Africa, the second team built a database of networks of corporations and persons involved in land transfer and ownership, focusing on Kenya’s public gazette document records. Their work will be used by journalists in Kenya to fight corruption and promote good governance of land resources.
Identifying CAFO characteristics using satellite imagery
This team partnered with Stanford Law School’s Regulation, Evaluation, and Governance Lab (RegLab) to leverage state-of-the-art advances in machine learning, artificial intelligence, and causal inference to design and evaluate programs, policies and technologies that modernize government regulations of wastewater polluters.
Please see the 2020 Highlights page for more on these projects and the Data Science for Social Good program!