] [Technical Paper
] [Police Chief Article
We use machine learning to build a better EIS. Machine learning is the ability of a computer to learn patterns in data and to use those patterns to make accurate predictions. It has been used over the past 30 years to solve thousands of problems, from driving cars to providing search results on Google. Machine learning can handle complex data from disparate sources, including dispatches, arrests, field interviews, training, demographics, crime, neighborhood features, and police and media narratives. This enables not only more accurate predictions but also a better understanding of what puts officers at risk.
Our system offers more flexibility. Our system provides continuous risk scores rather than binary flags. Risk scores enable the department to rank all officers by risk, from top to bottom; to explicitly choose tradeoffs (more correct flags versus more incorrect flags); and to allocate by available resources (for example, deciding which officers should receive department-wide training first versus deciding which officers should receive training).
Unlike threshold and outlier systems, our machine learning system allows the department how far ahead the system should predict, down to the next dispatch. This gives the department the ability to decide if it should send lower-risk officers to a call even if they’re farther away.
We have demonstrated our system’s performance in two departments. The Charlotte-Mecklenburg Police Department (CMPD) and Metropolitan Nashville Police Department (MNPD) gave us data on officer attributes (e.g. demographics, join date), officer activities (e.g. arrests, dispatches, training), and internal affairs investigations (the case and outcome). We supplemented their data with publicly available sources, such as American Community Survey data, weather data, shapefiles, and quality-of-life surveys. We then simulated history by showing how our system would have done if each department had used it.
We used CMPD’s EIS thresholds as the baseline for both departments (MNPD doesn’t have an EIS.) Our system correctly flags 10-20% more officers who go on to have adverse incidents while reducing incorrect flags by 50% or more.
The work for each department focused on slightly different tasks. With CMPD, we predicted all major adverse incidents, ignoring more minor violations such as uniform issues. With MNPD, we predicted sustained complaints and disciplinary actions.
We have updated the system to automatically incorporate supervisor feedback. Supervisors have expertise and information about officers that the EIS does not, so we are collecting their feedback on the EIS’s risk assessments and weighing their feedback based on their past accuracy.
CMPD and MNPD have both switched to using our EIS.
What We’re Doing
We are collecting better data on early interventions than what has been collected in the past. We will use those data to assess the effectiveness of the interventions and to provide tailored interventions for officers.
Are You Our Next Partner?
We’d like to partner with more departments that have the data, staff, resources, and willingness necessary to adapt and implement the model. To do this project, you will need at least three years of officer-level data:
- Officer ranks and assignments
- Internal affairs investigations and outcomes
- Data on whatever you want to predict (e.g. if you want to predict officer injuries, you need to share officer-injury data)
- Officer department violations
- Traffic stops
- Pedestrian stops
- Firearm use
- Response to resistance/use of force
- Citations written by the officer
- Field interviews
- Raid and search
- Knock and talk/stop and frisk activities
- District / beat boundaries
- Department policies and procedures
You will get more from the model if you also provide the following:
- EIS flags
- Officer education
- Race / ethnicity
- Marital status
- Age or DOB
- Secondary employment
- Officer criminal history
- Performance evaluations
- Psychological evaluations
- Veteran status
- Driving record
- Courses taken
- Each officer’s training officer
- Sick time, vacation time, overtime
- Claims and lawsuits
- Suspect info (demographics, possession of drugs, mental disorder, etc.)
- Calls for service and clearance
- Gang territory shapefiles
We have put this information and more into a spreadsheet here. We wrote a short note on computational requirements here. We posted directions on how to dump databases here. You can find copies of our standard contracts here.
You can hash officer identities (badge numbers, employee numbers, etc.) to provide another layer of protection while allowing us to match officer records across data sources.
Ready to Contact Us?
If you think you fit, please let us know.