Intersectional Data Analysis of Gun Violence in Boston: Teaching Data Activism to Mitigate Systemic Oppression.

Biased data is increasingly becoming a part of algorithms that deter- mine people's livelihood, such as predictive policing or recidivism predictors. One of the most effective ways of understanding how such algorithms work starts by examining the systems of oppression that lead to biased data. The lesson, "Intersectional Data Analysis: Examining Shootings in Boston", begins with examining the connection between racism, housing, and policing. Then, students use their data science skills to analyze how gun violence disproportionately harms African Americans. As a result, students examine the direct effects of historical bias embedded in data. The results show the student's ability to use data science and their knowledge of gun violence being a racial justice issue to create unbiased datasets, which may lead to fair algorithms.