William Isaac and Kristian Lum

From Los Angeles to New York, there is a quiet revolution underway within police departments across the country.

Just as major tech companies and political campaigns have leveraged data to target potential customers or voters, police departments have increasingly partnered with private firms or created new divisions to develop software to predict who will commit a crime or where crime will be committed before it occurs. While it may sound eerily reminiscent of the Tom Cruise movie Minority Report, the dream of having a tool that, in theory, could more efficiently allocate policing resources would be a boon for departments struggling with shrinking budgets and increasing pressure to be more responsive to their communities.

Policing the USA

Not surprisingly, a 2012 survey by the Police Executive Research Forum found that 70% of roughly 200 police agencies planned to implement the use of predictive policing technology in the next two to five years. The technology policy institute Upturn found that at least 20 of the 50 largest police agencies currently use predictive policing technology, and another 150 departments are testing the tools. But, in the rush to adopt these new tools, police departments have failed to address very serious Fourth Amendment concerns and questions of whether predictive policing reveals new "hot spots" unknown to the police or simply concentrates police effort in already over-policed communities.

Guilt by association, not evidence

Contrary to Hollywood’s depiction of predictive policing, police departments do not have a team of psychics who see crimes before they occur. In reality, the predictions are made by a computer program that uses historical police records to make estimates about future crime or criminals. Those predictions then become part of policing strategies that, among other things, send police commanders to people's homes to monitor their activity. In some cases, the suspects receiving additional monitoring have yet to be implicated in any crimes.

In the more commonly used place-based predictive policing, departments are provided estimates of potential crime “hot spots” or locations where a high number of recorded crimes are expected to occur. Departments allocate additional officers to patrol these areas. While these examples may seem like only minor modifications to standard policing practices, predictive policing tools bring about a fundamental shift in how police decide that certain people and communities are deserving of greater police scrutiny and who is accountable for these decisions.

Why whites must join fight for black justice: Voices

The Fourth Amendment, largely prohibits unreasonable searches and seizures. Many prominent civil liberties groups such as the American Civil Liberties Union have argued that the growth of predictive policing will allow officers in the field to stop suspects under the guise of “Big Data” rather than more stringent legal standards , such as reasonable suspicion. An even more egregious violation of the Fourth Amendment could come through the use of social media by law enforcement to monitor the contacts of known criminals. That could easily open the door to an even larger network of people being monitored who have actually committed no crime, but are seen by law enforcement as guilty by association. How often do we "friend" or "follow" folks whose past is largely a mystery to us?

Use of algorithms shows little crime reduction

More concerning is the lack of solid, independent evidence that predictive policing tools actually reduce crime or reveal hidden patterns in crime that were not already known to law enforcement. While one study co-written by the founders of the proprietary predictive policing software PredPol reported evidence of a small reduction in property crime through the use of their tool, a study conducted by RAND Corporation found no decline in crime rates after the deployment of a similar tool in Shreveport, La. Another peer reviewed study assessing the Chicago Police department’s algorithm that cranks out a suspicious subjects list, or “heat list,” also found no evidence that the program decreased the number of homicides in the city. What the study did find was that the program disproportionately targeted the city’s black residents.

In our new study, we apply a predictive policing algorithm to publicly available data of drug crimes in the city of Oakland, Calif. Predictive policing offered no insight into patterns of crime beyond what the police already knew. In fact, had the city of Oakland implemented predictive policing, the algorithm would have repeatedly sent the police back to exactly the same locations where police had discovered crime in the past. Through over-policing, these tools have the potential to cause greater deterioration of community-police relations, particularly in already over-policed communities.

While the promise of predictive policing is alluring, the reality is that predictive policing tools raise serious legal and technical issues and appear ineffective at reducing crime.

We need much more public scrutiny and research into the efficacy and community impact of predictive policing tools before they are further deployed across the country. Police departments and private vendors should release findings of their internal assessments (if conducted) as well as related data for third-party replication and assessment. Departments and private vendors should also engage a wider group of community stakeholders when departments conduct internal testing of new predictive tools and aggressively develop intervention and training protocols for patrol officers that minimize negative police interactions if they intend to use predictive policing tools.

The reality is that many police departments – like many government agencies and large businesses – lack the institutional experience and resources to seamlessly evolve into a data-driven organization. This does not mean departments should reject data-driven approaches. Instead they should become more aware of flaws and biases embedded within data and ensure that decision-makers understand the limits of its use.

Future efforts of data-driven policing should be inclusive — a process that leverages all of a city's resources to improve community safety.

William Isaac is a doctoral candidate in the Department of Political Science at Michigan State University. Kristian Lum is the lead statistician at the Human Rights Data Analysis Group.