Expanding Our Early Intervention System for Adverse Police Interactions

Fellows: Sumedh Joshi, Jonathan Keane, Joshua Mausolf, Lin Taylor
Data Science Mentor(s): Jen Helsby, Joe Walsh
Project Manager: Allison Weil
Project Partner: Metro Nashville Police Department

Many police departments in the United States use “early intervention systems” to identify officers who may benefit from additional training, resources, or counseling. These systems attempt to determine behavioral patterns that predict a higher risk of future adverse incidents, ranging from excessive use of force and citizen complaints to on-duty accidents and personal injury. Detecting these risk factors enables departments to develop targeted interventions and make operational changes to protect officer safety and improve police/community interactions.

In 2015, DSSG worked with the Charlotte-Mecklenburg Police Department on building a better early intervention system, applying data analysis to provide insights on individual and situational risk factors for adverse interactions. In 2016, we partnered with additional police departments, including the Metro Nashville Police Department, to test and expand this work in new municipalities, improving both the overall model and local performance. Like our work in 2015, we used anonymized police data and contextual data about local crime and demographics to detect the factors most indicative of future issues, so that departments can provide additional support to their officers.

In the months since the summer ended, both departments continue to work with the Center for Data Science and Public Policy on implementing the new EIS. The Nashville team gave their department a list of the highest-risk officers according to our model, which MNPD subsequently used to send letters to the officers and their supervisors informing them of the results and specific risk factors that led to their score. We’re now helping them integrate the EIS into their existing IT system, so that it will continuously update with new data.

Similarly, CMPD awarded us a contract to help implement our EIS on their system. We’re building a web interface to help them and other partners evaluate and understand the performance of the models. The interface will also allow for feedback from supervisors in the department on the quality of the predictions, providing valuable new data to further refine the model. CMPD hopes to bring the system live in the coming months. In addition, the Pittsburgh Bureau of Police will be involved in the expansion of this EIS in early 2017.

You can find updated information about this project here.