Best Practices in
Data-Driven Policing Workshop

On June 23 and 24, 2022, we organized a workshop hosted by Northwestern University's Center for Advancing Safety of Machine Learning aimed at developing a list of best practices for the development and deployment of place-based crime forecasting systems that are ethically informed and empirically grounded.

 

This workshop represents the culmination of a three-year project at Cal Poly, San Luis Obispo, and the University of Florida, funded by the National Science Foundation. We are also combining efforts with an ongoing project with Northwestern University and Underwriters Laboratories to develop methods of measuring the human impacts of machine learning technologies (casmi.northwestern.edu/).

 

Data-driven policing has been the subject of public scrutiny and suspicion because of concerns about privacy, bias, transparency, and community impacts, among others. We believe that investigating these issues in an interdisciplinary forum involving academics, technologists, and law enforcement officers will foster a productive conversation, including generating recommendations for both the companies who develop these technologies and the practitioners who use them. As data-driven policing technologies are rapidly becoming police orthodoxy, these discussions will help anticipate the public’s concerns and safeguard the benefits of these tools.

 

The workshop will consist of a series of sessions, including brief presentations followed by lightly moderated discussion targeting different concerns with data-driven policing technologies. Because we expect to have attendance from multiple technology vendors and police departments in this space, along with a range of academic disciplines, we expect our conversations at the workshop to be concrete, practical, and forward-looking. Our goal is to parry critiques of predictive policing into practical recommendations to shepherd the ethically sensitive design and use of data-driven policing technologies. Our primary work output will be a report to be published in the summer of 2022, followed by efforts to engage policymakers, law enforcement agencies, and ordinary citizens across the country.

Acknowledgements

This work has been funded by U.S. National Science Foundation awards #1917707 and #1917712, “Artificial Intelligence and Predictive Policing: An Ethical Analysis.” The project is also supported by Cal Poly, San Luis Obispo, the University of Florida, Northwestern University via a generous gift from Underwriters Laboratories, and many other contributors.

Participants

ShotSpotter

Paul Ames

Kansas City Police Department

Jonas Baughman

Florida International University

Clinton Castro

Northwestern University

Alexander Einarsson

Northwestern University

Kristian Hammond

Axon

Yasser Ibrahim

Cal Poly

Ryan Jenkins

Policing Project at NYU Law

Mecole Jordan-McBride

University of Michigan

Renée Jorgensen

Policing Project at NYU Law

Katie Kinsey

Northwestern University

Dan Linna

Underwriters Laboratories

Monica Mena

Center on Race, Inequality, and the Law, NYU School of Law

Terrance Pitts

University of Florida

Duncan Purves

Northwestern University

Sarah Spurlock

University of Florida

Schuyler Sturm

RAND Corporation

Dulani Woods

Copyright Cal Poly 2020

Ryan Jenkins

Associate Professor

Duncan Purves

Associate Professor