The improvements in computing capabilities and the availability of sharing large amounts of data provide real-time enhancements in policing. Shared data through CAD-to-CAD systems, the ability to query neighboring agencies' records management systems, using algorithms to find geographic and other crime patterns and artificial intelligence reviewing material while creating transcripts and finding anomalies are all instances of using updated technology to improve policing.
Officers have long known where the hotspots for types of crime are within their jurisdiction. Increased data-sharing, data gathering, and improved algorithms could combine even more data to support what many police officers already knew about their area and could even provide a new perspective.
Artificial Intelligence
The improvements in AI allow software to process large amounts of data to find commonalities, patterns, similarities, and trends. Combing through hundreds of reports, license plate reader data, and querying or calling other agencies can take several staff hours. With artificial intelligence (AI), the data can be reviewed much more quickly, and the information can be extrapolated for review.
Some of the current programs using AI and updated algorithms continue to provide options for law enforcement - options like the ones below that can save staff time while improving public safety through technology.
- Video surveillance – systems that provide facial recognition, biometrics, smart cameras that follow motion, aerial surveillance drones, and high-quality video that AI can monitor in real-time and warn of impending threats or flag suspicious video for timely review.
- Locational data – real-time crime mapping and gunshot detection software collect information and review it quickly to determine the locations officers need to focus on. Technology is also improving and becoming more adept in detecting growing crowds so officers can be updated proactively.
- Field data – including body-worn camera footage, real-time reporting and updating of the call through the computer-aided dispatch (CAD) system, and reports filed from the field. With CAD-to-CAD sharing, the jurisdictional borders that bad guys may cross are also open for artificial intelligence to query and consider as part of the analysis.
- Training – Review of body camera footage by a program designed to review every frame of video for behavioral concerns and create a transcript of the incident frees up supervisory time needed to spot-review the footage. The flagged video does not indicate wrongdoing, but it can save staff time while reviewing all the footage and flagging anything of concern for further review. Quality assurance of dispatch recordings can also provide the same review through AI, listening to the calls and looking for any flags that require more review.
Forecasting
The often-overused phrase, data-driven policing, encompasses the use of data, with more data being better, to make informed decisions on policing. Much like the phrase predictive policing that sought proactive policing through data analysis in the mid-2000s, both seek to use data to make the best use of available staff and resources while seeking to deal with crime proactively. The proactive part of policing is the most difficult aspect and technology is helping law enforcement make those decisions.
The increased points of data available allow AI to determine hotspots for criminal activity and even narrow down suspects based on data mining. One of the considerations with using existing data is knowing there may be biases included in the data. Much like forecasting the weather uses historical data and includes scientific analysis of current conditions to predict upcoming weather concerns, predictive policing or data-driven policing seeks to prepare today for what may happen tomorrow. As with forecasting the weather, policing needs to use outside sources and other evidence as a guide to the final decision.
The future through technology
The future of policing relies on technology. A recent study determined that AI technologies could reduce crime in a city by 30 to 40 percent. As artificial intelligence improves and data-sharing between systems within the same agency and the systems of other agencies becomes commonplace, law enforcement may be able to predict crime and crime hotspots more accurately.
As we begin to embrace technology fully and AI finds its footing in policing, not only do we need to heed changing legal circumstances surrounding privacy laws and similar cyber mandates, but we need to share that data with other law enforcement agencies. Criminals do not seem to mind crossing jurisdictional boundaries, and police data should also cross those geographic lines to keep the public and responders safe.
Toni Rogers
Toni Rogers is a freelance writer and former manager of police support services, including communications, records, property and evidence, database and systems management, and building technology. She has a master’s degree in Criminal Justice with certification in Law Enforcement Administration and a master's degree in Digital Audience Strategies.
During her 18-year tenure in law enforcement, Toni was a certified Emergency Number Professional (ENP), earned a Law Enforcement Inspections and Auditing Certification, was certified as a Spillman Application Administrator (database and systems management for computer-aided dispatch and records management), and a certified communications training officer.
Toni now provides content marketing and writing through her company, Eclectic Pearls, LLC.
