AI & Public Data for Humanitarian and Emergency Response

ABSTRACTWhen an emergency event, or an incident relevant for peacekeeping or humanitarian needs first occurs, getting the right information as quickly as possible is critical in saving lives. When an event is ongoing, information on what is happening can be critical in making decisions to keep people safe and take control of the particular situation unfolding. In both cases, first responders, peacekeepers, and others have to quickly make decisions that include what resources to deploy and where. Fortunately, in most emergencies, people use social media to publicly share information. At the same time, sensor data is increasingly becoming available. But a platform to detect emergency situations and deliver the right information has to deal with ingesting thousands of noisy data points per second: sifting through and identifying relevant information, from different sources, in different formats, with varying levels of detail, in real time, so that relevant individuals and teams can be alerted at the right level and at the right time. In this talk I will describe the technical challenges in processing vast amounts of heterogenous, noisy data in real time from the web and other sources, highlighting the importance of interdisciplinary research and a human-centered approach to address problems in humanitarian and emergency response. I will give specific examples and discuss relevant future research directions in Machine Learning, NLP, Information Retrieval, Computer Vision and other fields, highlighting the role of knowledge combined with Neural and other approaches. This talk will present an overview, and draw from some of our publications at CVPR, AAAI, EMNLP, and others.