Data science for good means designing for people: Part 1

So many of the data projects making headlinesfrom a new app for finding public services to a new probabilistic model for predicting weather patterns for subsistence farmersare great accomplishments but dont seem to have end users in mind. Discover how organizations are designing with, not for, people, accounting for what drives them in order to make long-lasting impact.

DataKind founder and executive Jake Porway hosts two back-to-back sessions that explore diverse examples of how organizations are applying data science for good by designing for people. In this first session, Rachel Quint, Jeremy Anderson, and Anh Bui explain how their organizations approach designing with people—accounting for their habits, their data literacy level, and, most importantly, for what drives them in order to make long-lasting impact. Rachel discusses measuring the UN sustainable development goals (SDGs) and the data needed to implement them, explaining how to collect data to help achieve success rather than simply monitoring it. Jeremy talks about his work at IBM’s Spark Technology Center and his approach to design thinking for social change. Ahn demonstrates that beyond human-centered design is ecosystem-engaged design. Ahn outlines how Benetech Labs builds solutions in context and in concert with the ecosystem, using a community focus to take a systems approach to problems, catalyzing and convening a community of active participants in building tech for good.