Rewiring Police Officer Training Networks to Reduce Forecasted Use of Force
Police use of force has become a topic of significant concern, particularly given the disparate impact on communities of color. Research has shown that police officer involved shootings, misconduct and excessive use of force complaints exhibit network effects, where officers are at greater risk of being involved in these incidents when they socialize with officers who have a history of use of force and misconduct. Given that use of force and misconduct behavior appear to be transmissible across police networks, we are attempting to address if police networks can be altered to reduce use of force and misconduct events in a limited scope.
In this work, we analyze a novel dataset from the Indianapolis Metropolitan Police Department on officer field training, subsequent use of force, and the role of network effects from field training officers. We construct a network survival model for analyzing time-to-event of use of force incidents involving new police trainees. The model includes network effects of the diffusion of risk from field training officers (FTOs) to trainees. We then introduce a network rewiring algorithm to maximize the expected time to use of force events upon completion of field training. We study several versions of the algorithm, including constraints that encourage demographic diversity of FTOs. The results show that FTO use of force history is the best predictor of trainee's time to use of force in the survival model and rewiring the network can increase the expected time (in days) of a recruit's first use of force incident by 8%.
We then discuss the potential benefits and challenges associated with implementing such an algorithm in practice.
AFOSR MURI grant FA9550-22-1-0380
Indiana University Racial Justice Research
- Doctor of Philosophy
- Computer Science