The Analytics of Social Progress: When Machine Learning meets Human Problems

In recent years, machine learning has fueled advances in logistics, retail, media consumption, health care, finance and banking. These solutions have earned relatively few people a lot of money!

Can machine learning also be applied to social problems for the betterment of all?

Amy Hepner, Data Scientist at Rhiza, will discuss four real world problems tackled this past summer by Data Science for Social Good fellows (dssg.io (http://dssg.io/)):

1.) Identifying police officers at an increased risk of adverse public interaction

2.) Early detection of high school students not on track to graduate

3.) Locating blighted homes in Cincinnati, and

4.) Targeting supporters for online political activism and fundraising

Focusing on the opportunities and risks of applying math to human service, we will discuss the basics of “supervised machine learning” and the implications of applying math to community: when is it helpful and how can we assure that it is not perpetuating systemic bias?

This talk requires little to no experience in math, computer science or statistics. Come if you’ve always wondered what machine learning/analytics really means, or if you are interested in math solutions to social justice problems.