Big Data Policing Dilemma

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This is the era of big data policing, to a large extent, aimed at determining where to allocate police presence. Predictive policing technology uses algorithms to pore over massive amounts of data to predict when and where future crimes will occur. These algorithms can guesstimate the times and locations of crimes, the potential perpetrators, and even their upcoming victims based on a variety of risk factors. 

For example, if the system recognizes a pattern of physical altercations outside a bar every Saturday at 2am, it could suggest increasing police presence there at that time to prevent the fights from occurring. 

Used by police across the United States for almost a decade, predictive policing relies on algorithms to interpret police records, analyzing arrest or parole data to send officers to target chronic offenders, or identifying places where crime may occur.

California’s Santa Cruz has become the first U.S. city to ban predictive policing. Critics says it reinforces racist patterns of policing – low-income, ethnic minority neighbourhoods have historically been overpoliced so the data shows them as crime hotspots, leading to the deployment of more police to those areas, according to international news agencies.

Predictive policing is nothing new, but currently, law enforcement agencies and the private companies who develop predictive algorithms utilize cutting-edge, computer driven models that can tap into massive stores of data and information. 

Predictive policing can be based on patterns regarding places, persons, or groups. 

The PredPol software package which has been adopted by police departments across the US reportedly looks at a narrow set of related statistics, giving additional weight to more recent events, to predict where and when crimes will occur during a given officer’s shift within a 150m by 150m square. The technology reportedly lowered crime rates. However, within a few years, the PredPol system has been abandoned by numerous departments because it didn’t help solve crime.  

The NYPD, America’s largest police force, was another early adopter of predictive policing algorithms. Developing its own algorithm suite inhouse, the NYPD used algorithms to forecast shootings, burglaries, robberies, etc., as of 2017.

Predictive policing poses many of the same civil and constitutional risks seen in other algorithmic law enforcement schemes like automated facial recognition, according to endgadget.com. As these algorithms are trained using data produced by the police, implicit biases held by those departments can worm their way into the output recommendations.