AI4SG collaboration between UMass and the Red Cross matures

  • In March 2022, Subhransu Maji, co-director of the Computer Vision Lab at the University of Massachusetts, Amherst, attends the AI4SG Dagstuhl seminar. Subhransu: “At the seminar, I started collaborating with folks from the Red Cross on an ML tool to assess damage of buildings and infrastructure in the wake of natural disasters.”
  • Fast-forward to Summer 2022: Yunfei Luo, Siddhi Brahmbhatt and Shruti Chanumolu, UMass students in the Data Science for Common Good program that’s led by Thomas Bernardin, continue the work on the Red Cross project (with help from Dagstuhl attendee Sara Beery (Caltech)).
  • November 2022: the UMass Center for Data Science announces that data scientist Virginia Partridge and MS student Tanmay Agrawal will dedicate time to this project. Jacopo Margutti from the Red Cross is excited: “Having such a serious, longer-term commitment from an academic partner is an enormous boost for our work in the AI/ML space. We’ve already started using the damage assessment tool in real life, but it can certainly benefit from further expansion and refinement.”
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Artificial Intelligence for Social Good (AI4SG) Seminar at Dagstuhl 2022

Artificial Intelligence (AI) and Machine Learning (ML) researchers from various universities with representatives from NGOs based in Benin, Tanzania, Uganda, The Netherlands and globally came together over a 5-day seminar in Dagstuhl, Germany to pursue various social good goals, such as improving air quality, increasing agricultural productivity with the help of technology, transforming health care, providing humanitarian support, and defeating poverty. The seminar facilitated the exploration of possible collaborations between AI and ML researchers and NGOs through a two-pronged approach. This approach combined high-level talks and discussions on the one hand with a hands-on hackathon on the other hand. High-level talks and discussions focused first on the central concepts and theories in AI and ML and in the NGOs’ development work, before diving into specific issues such as generalisability, data pipelines, and explainability. These talks and discussions allowed all participants – in a very short time-frame – to reach a sufficient level of understanding of each other’s work. This understanding was the basis to then start investigating jointly through a hackathon how AI and ML could help addressing the real-world challenges presented by the NGOs.

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