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| What is predictive policing? |
| Understanding the concerns |
| Navigating the future with data |
Law enforcement faces numerous challenges. One of the most daunting is a lack of adequate staffing. Data gathered in 2023 by the Police Executive Research Forum found that “agencies are losing officers faster than they can hire new ones, so total sworn staffing has continued to decline.” This shortage is pushing police forces to find ways to fulfill their essential mission more efficiently. One of the most innovative is a data-driven approach called predictive policing.
Public safety is a major issue for law enforcement agencies working in government. And since it involves public safety, many other sectors need to consider it as one of the risks their organizations need to manage. Those include the safety of employees and facilities.
Predictive policing has critics as well as proponents. Still, digital technology and data analytics are transforming policing, and we’ll explore what that means for the future of law enforcement and public safety.
What is predictive policing?
Predictive policing analyzes large amounts of data to help anticipate and prevent future crime. It uses statistical predictions and algorithms to identify crime hot spots and individuals who are at high risk of committing or becoming victims of crimes.
Predictive policing methods can be broken down into three categories, though in practice, they are interrelated:
- Place-based. This method uses crime data to identify places and times in the community that have historically demonstrated a high crime risk.
- Person-based. This method analyzes and assesses risk factors such as past arrests or victimization patterns.
- Group-based or network. This approach is similar to person-based, except that it focuses on groups of individuals, such as gang members or organized crime “families.”
One of the key reasons for the interest in and use of predictive policing is the ever-increasing power of digital technology to help with decision-making, specifically its capability to gather and analyze massive quantities of data. Proponents of predictive policing cite several benefits. The most compelling fall under two headings:
Potential reduction in certain types of crime
This, of course, is the main reason why predictive policing has attracted attention. By allowing law enforcement to be proactive rather than reactive, it can help lower crime rates by preventing criminal activity before it happens. Property crimes in particular have demonstrated the strongest results.
More efficient resource allocation
By incorporating a predictive policing approach, police departments can shift more prevention and investigative work to modern technology. This can help departments optimize officer scheduling and deployment, balancing workload and coverage. It can also boost the cost-effectiveness of departments with limited budgets and personnel shortages. Through cost-benefit analyses of policing strategies, data analytics can help agencies better conduct long-term budgeting.
Several technology developers have created predictive policing products. One such tool, which focuses on potential terrorism, collects data from surveillance cameras, vehicle license plate readers, and other digital sources, using databases and camera feeds to assemble surveillance maps. Other tools can “design” tactical responses to potential future criminal activity. Since there is currently no standardization or regulation, the capabilities of predictive policing technology vary widely.
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Understanding the concerns
Several U.S. cities have claimed that predictive policing have strengthened public safety efforts. In 2016, the New York Police Department launched its Strategic Prevention and Response Unit, which identified high-risk individuals while deploying community outreach teams offering social services to discourage violent responses. In the first two years, the NYPD stated, murders decreased by 5.1% in the program’s targeted areas. In 2017, the Chicago Police Department introduced a predictive policing program that dispatched patrols and social service teams to data-identified high-risk areas. The CPD reported a 23% decline in homicide rates in the first year of the system’s use.
But predictive policing has also its critics, who argue that the approach hasn’t truly delivered on all its promised benefits. A New York University study that examined 13 U.S. jurisdictions found that predictive policing systems exacerbated existing discriminatory law enforcement practices. An analysis of predictive policing software used in Plainfield, N.J., found crime prediction “rarely lined up with reported crimes.” And a Brennan Center for Justice report noted that Los Angeles and Chicago ended what had once been highly touted programs when they were found to be ineffective over time.
Many of these concerns revolve around the issue of algorithmic fairness. Critics say that predictive policing discriminates against people of color and economically disadvantaged groups. Arrest data, particularly for drug and nuisance crimes, can be influenced by racial bias in police officers’ choices about whom to investigate. Data analytics algorithms also may “predict” a higher incidence of crime in minority communities than actually exists. Police then focus on those communities, thus adding to “dirty data” that reinforces their status as hot spots for crime.
Another worry of critics (and even some proponents) of predictive policing involves data privacy. Law enforcement data is highly sensitive. Though some data can and should be shared with the community for the sake of transparency, most needs to be secured so that it doesn’t fall into fraudsters’ hands. That requires departments to comply with regulations for protecting sensitive information.
The question these concerns raise: How can law enforcement navigate predictive policing ethically and responsibly, keeping the community safe while still maintaining its trust? After all, policing should reduce risk, not increase it.
Navigating the future with data
Predictive policing is data-driven. It’s also technology-driven. In an effort to overcome algorithmic bias, public safety agencies are using artificial intelligence (AI) to enhance the accuracy of their predictive analytics. AI-generated algorithms can “learn” and improve as they’re exposed to more data and patterns.
That said, law enforcement departments can’t assume that the machines will ever get everything right on their own. Data-driven policing can be effective only if agencies use it as a tool—and not the only one in their crime prevention toolbox.
Given both the benefits and the risks of predictive policing, practitioners have been developing best practices for its use. The most notable include:
- Human oversight. Experts recommend that agencies be aware that algorithms need to be adjusted—by humans, not machines—to reduce racial and other biases. Relying too much on machine-analyzed data can result in distorted analysis that can work against successful crime reduction. (For instance, departments should put less reliance on arrest data alone.) That makes it essential for agencies to train officers and investigators to use data-driven technology correctly and effectively.
- Community involvement. Police departments require the help and cooperation of the communities they serve to solve criminal cases and reduce their incidence. By using data that identifies community problems and needs, police can develop more effective outreach programs.
- Transparency. Sharing the data that predictive policing gathers—and demonstrating how these systems work—builds trust between community members and law enforcement. It also encourages citizen cooperation in crime prevention efforts.
- Regular auditing. Police departments should continually monitor and measure the effectiveness of their predictive policing initiatives and root out algorithmic bias.
Data-driven policing strategies can help drive crime prevention efforts in ways that go beyond prediction–while still making use of its predictive capabilities. These applications include analyzing data from body cameras and license plate readers, as well as the establishment of real-time crime centers. Predictive policing techniques can also improve how law enforcement aids businesses in the battle against fraud.
Data analytics has a significant role to play in public safety. But it’s critical that law enforcement know about and mitigate the risks of data-driven policing.
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