Law, Order and Algorithms: Understanding Racial Patterns in Police Data

This Webinar was originally broadcast on:
June 19, 2017 Enroll Now
Watch and listen to the original one-hour Webinar in its entirety. This Webinar recording features the full presentation led by Poynter faculty and visiting faculty including Q&A from the audience and resources from the presenter.

Course Overview

Title:
Law, Order and Algorithms: Understanding Racial Patterns in Police Data
Type:
Webinar
Cost:
This $29.95 webinar is free thanks to the support of the McCormick Foundation.
Save 20 percent on all Poynter training by becoming a Poynter Prepared Member. Learn more and register now.
Originally Broadcast On:
June 19, 2017
Time Estimate:
One hour for the main presentation and questions. Sometimes presenters stay longer to answer additional questions from participants.

About Webinars

In this virtual classroom, participants can join in a seminar led by Poynter faculty and visiting faculty. This screencast includes live audio and a slideshow presentation in which participants can post questions and respond to poll questions posed by the host.

This webinar will give you an overview of a new, open-source project, Stanford Open Policing, that aims to provide journalists and researchers with data on police stops and the best practices for analyzing the information.

We will walk you through the data (more than 100 million stops across 30 states) and discuss the types of stories that you can tell about the intersection of race and policing in the U.S.

What Will I Learn:
  • How to access the data
  • How to interpret and analyze the data
  • Key terms when looking at race and policing, for example, understanding the differences between disparate impact and discrimination.
  • Story ideas from the data
Who Should Take this Course:

Journalists, agencies, academic researchers and others interested in the intersection between race and policing in America will gain a deeper understanding of how to evaluate patterns in police stops.

Course Instructors:

Cheryl Phillips

Cheryl Phillips is the Lorry I. Lokey Visiting Professor in Professional Journalism in the Department of Communication at Stanford University. She teaches data journalism and investigative journalism and is a founding member of the Stanford Computational Journalism Lab, where she works to lower the cost of accountability journalism. The effort to collect, process and analyze local datasets and then help tell journalistic stories from that data is part of the lab’s work. She has used police stop data in her journalism career to track possible disparities and bias in policing.

Sharad Goel

Sharad Goel is an assistant professor at Stanford University in the School of Engineering. He draws on ideas from computer science and statistics to analyze and design public policy. His recent work examines racial disparities in policing, the use of algorithms in the criminal justice system, allegations of voter fraud, and online privacy.

Sponsor:

Robert R. McCormick Foundation

The Robert R. McCormick Foundation is a nonprofit organization committed to fostering communities of educated, informed and engaged citizens. Through philanthropic programs, Cantigny Park and museums, the foundation helps develop citizen leaders and works to make life better in our communities. The foundation was established as a charitable trust in 1955, upon the death of Col. Robert R. McCormick, the longtime editor and publisher of the Chicago Tribune. The foundation is one of the nation's largest foundations, with more than $1 billion in assets.

Training Partner:

Stanford University

Stanford University, located between San Francisco and San Jose in the heart of California's Silicon Valley, is one of the world's leading teaching and research universities. Since its opening in 1891, Stanford has been dedicated to finding solutions to big challenges and to preparing students for leadership in a complex world.

Technical Requirements:

Windows
1.4GHz Intel® Pentium® 4 or faster processor (or equivalent) for Windows 7, Windows 8 or Windows 8.1
512MB of RAM (1GB recommended) for Windows 7 or Windows 8
Microsoft Internet Explorer 8, 9, 10, 11; Mozilla Firefox; Google Chrome
Adobe® Flash® Player 11.2+

Mac
1.83GHz Intel Core™ Duo or faster processor
512MB of RAM (1GB recommended)
Mac OS X 10.7.4, 10.8, 10.9
Mozilla Firefox; Apple Safari; Google Chrome
Adobe Flash Player 11.2+

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