mw10104587 / compjournalism-hw3

HW3 of Computational Journalism, Columbia University

Analyze Stop and Frisk for Racial Bias

For this exercise, you will break into three groups. Two will analyze data, and the third will do legal research. The data for this assignment is available here. All groups will turn in their assignment by posting on this blog — you’ll have logins shortly — and linking to a github repository of your code.

All groups: How can we encode our ideas about racial fairness into a quantitative metric? That is the fundamental question underlying this assignment and you must answer it. Don’t build models that give you answers to useless questions; each model you build must embody some justifiable concept of fairness. Many such metrics have been proposed; part of the assignment is researching and evaluating them. I’ve proposed some models that might be interesting, but I’ll be just has happy — perhaps happier — to have you tell me why these particular model formulations will not yield an interesting result. Also, you have to tell me what your modeling results mean. Is there bias? In what way, how significant is it, and what are alternate explanations? Uninterpreted results will not get a passing grade.

Learning with paper

Gibson and Wu Data Original Paper Implementation Code