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Data Science for Social Good Seeks 2017 Applicants

By Rob Mitchum // January 18, 2017

For the past four years, the Data Science for Social Good summer fellowship has brought over 160 fellows from around the world to Chicago to work on data-driven projects with real world significance. They’ve used machine learning and predictive analytics to improve graduation rates, anticipate urban blight and water infrastructure issues, direct hazardous waste inspections, deliver targeted social services, and much more. Now, the program, run by the CI’s Center for Data Science and Public Policy, is looking for fellows, mentors, project managers, partners, and funders for 2017, with applications due at the end of January.

One example of the type of socially relevant work you can do at DSSG is the police project, which has expanded from its origin in the summer of 2015 to real world implementation and collaborations with multiple police departments. Teams in 2015 and 2016 worked with the Charlotte-Mecklenburg (CMPD) and Metro Nashville (MNPD) police departments, using their data to build a model that predicts officers at elevated risk of a future adverse event, such as an unjustified use of force or citizen complaints.  This “early intervention system” (EIS) is now being tested in the field, according to a new update published at the DSSG website.

In the months since the summer ended, both departments continue to work with the Center for Data Science and Public Policy on implementing the new EIS. The Nashville team gave their department a list of the highest-risk officers according to our model, which MNPD subsequently used to send letters to the officers and their supervisors informing them of the results and specific risk factors that led to their score. We’re now helping them integrate the EIS into their existing IT system, so that it will continuously update with new data.

Similarly, CMPD awarded us a contract to help implement our EIS on their system. We’re building a web interface to help them and other partners evaluate and understand the performance of the models — trying to avoid the “black box” mystery of some machine learning predictions. The interface will also allow for feedback from supervisors in the department on the quality of the predictions, providing valuable new data to further refine the model. CMPD hopes to bring the system live in the coming months. In addition, the Pittsburgh Bureau of Police will be involved in the expansion of this EIS in early 2017.

One of the fellows who worked on the Nashville police project last summer, Lin Taylor, has also written a blog post about her experience. The article goes beyond the technical accomplishments of the summer to Taylor’s takeaways about the fellowship’s culture, community, and collaborative spirit.

DSSG exceeded my expectations. I got to learn from and interact with a community of people who care about using their skills to do social good, some of whom I expect to be friends with for life. The project itself was exciting and I learned a lot about what it takes to work with data science on social problems (hint: it’s not straightforward!). We were also given loads of opportunities to present our work to the other fellows and at meetups with the local tech community. The outcome of the projects themselves was only one small part the wider goal of training and education. As far as making an impact is concerned, DSSG is playing the long game.

For more information on getting involved in Data Science for Social Good, and to find links to applications, visit their in-depth FAQ page.