by Olivia Zhu
These pieces merely scratch the surface of the technologies and methods required for predictive policing. The majority of discussions in this space focus on the ethics involved: Are the results increasing instances of racial profiling? Does the practice violate Fourth Amendment rights?
But here’s the thing.
Although predictive policing is in its infancy with regard to adoption and success, there’s far more to it—and there are better questions to be asking.
For example, journalists have wondered about the quality of the data that police departments are shifting into newly purchased software programs. It’s certainly not wrong to state that predictive models will only be as good as the data that serves as their foundation. Nevertheless, assessors have quibbled over whether the data that police departments collect under- or over-represents poor, often minority-dominated communities.
One theory goes that these underserved communities don’t trust the police, and thus are less likely to report crime—making it less likely they’ll be served by any of the benefits of predictive policing. Conversely, perhaps police presuppose that certain neighborhoods are more prone to crime, and decide to patrol them more frequently. That, in turn, increases the likelihood that more incident reports are filed for the region. Predictive models suggest more patrols in these areas, and racial profiling may occur as a result.
The very first set of questions that should be asked, then, is: “How can we determine if under- or overreporting is happening?” “Do reporting trends vary by type of crime?” and “Once we know, can we fold the knowledge into effective predictive policing programs?”