Delivery models for accelerating data science for social good
Delivery models for accelerating data science for social good

Abstract: 

The rise of data science has been largely fueled by the promise of changing the business landscape - enhancing one's competitive advantage, increasing business optimization and efficiency, and ultimately delivering a better bottom-line. This promise reaches across sectors as machine learning methods are getting better, data access continues to grow, and computation power is easily accessible. However, because the practice of doing data science can be expensive, there is a danger that this so-called promise of data science may only be available to the most well-resourced organizations with sophisticated data capabilities and staff. For the past five years, DataKind has been working to ensure social change organizations too have access to data science, teaming them up with data scientists to build machine learning and artificial intelligence solutions that aim to reduce human suffering. In doing so, DataKind has learned what it takes to apply data science in the social sector and the many applications it has for creating positive change in the world. This session presents DataKind projects showcasing the wide range of applications for ML/AI for social good. From using satellite imagery and remote sensing techniques to detect wheat farm boundaries to protect livelihoods in Ethiopia, to leveraging NLP to automate the time consuming process of synthesizing findings from academic studies to inform conservation efforts and to classifying text records to better understand human rights conditions across the world to using machine learning to reduce traffic fatalities in U.S. cities, learn about some of the latest breakthroughs and findings in the data science for social good space and learn how you can get involved

Bio: 

Caitlin can point to a single unifying phrase to sum up her work: “that which isn’t measured cannot be improved.” For the past decade, she’s been applying Lord Kelvin’s words to projects across sectors – from energy and climate change to education and social good. Caitlin has a penchant for wearing many hats: a day job leading impact measurement at an education company, PhD work in energy technology evaluation, and volunteer projects with a number of non-profits—including DataKind. Through the wrangling and analysis of vast data sets, she’s thrilled to be applying the rules of measurement and evaluation to problems that are sectoral in scope and that have the potential to positively impact so many people’s lives. Caitlin is passionate about science communication and can be seen trying to transfer (and translate) her research to decision-makers and practitioners across the globe at conferences and workshops. She holds multiple degrees from the University of Miami. Go ‘Canes!

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