Better Bridges Between Model and RealWorld

Abstract

To build better machine learning solutions, we need not only better models but also better bridges through the inputs and outputs to the real world challenges they aim to solve. On the input side, these bridges are the tools one has to work with the available data. On the output side, they are the tools to evaluate and ensure models will generalize and work as intended. This project aims to improve our understanding and resources on both sides, with a particular focus on social good applications. So far, progress has been made in 6 contexts, such as countering misinformation and human trafficking. Future work aims to both build on these specific contexts, as well as leverage their interconnections to produce insights and tools with wide applicability.