Reform predictive policing

for more-equitable law enforcement are growing. In the United States, police reform has become both politically charged and divisive following numerous publicized cases of police violence towards unarmed African Americans. At the same time, tight policing budgets are increasing demand for law-enforcement technologies. Police agencies hope to ‘do more with less’ by outsourcing their evaluations of crime data to analytics and technology companies that produce ‘predictive policing’ systems. These use algorithms to forecast where crimes are likely to occur and who might commit them, and to make recommendations for allocating police resources. Despite wide adoption, predictive policing is still in its infancy, open to bias and hard to evaluate. Predictive models tie crimes to people or places. Offender-based modelling creates risk profiles for individuals in the criminal justice system on the basis of age, criminal record, employment history and social affiliations. Police departments, judges or parole boards use these profiles — such as estimates of how likely a person is to be involved in a shooting — to decide whether the individual should be incarcerated, referred to social services or put under surveillance. Geospatial modelling generates risk profiles for locations. Jurisdictions are divided into grid cells (each typically around 50 square metres), and algorithms that have been trained using crime and environmental data predict where and when officers should patrol to detect or deter crime. Criminologists, crime analysts and police leaders are excited about the possibilities for experimentation using predictive analytics. Surveillance technologies and algorithms could test and improve police tactics or reduce officer abuses. But civilrights and social-justice groups condemn both models. Offender-based predictions exacerbate racial biases in the criminal justice system and undermine the principle of presumed innocence. Equating locations with criminality amplifies problematic policing patterns. Researchers and observers need to stay vigilant as these technologies become more integrated and prescriptive. As an ethnographer, I have conducted research with Azavea, a software firm in Philadelphia, Pennsylvania, that sells a predictive policing suite called HunchLab. I was interested in how the firm evaluates its product and found that it was operating in good faith, aiming to use analytics to improve policing, public safety and officer accountability. But there are no guarantees that such voices will prevail. Many policing bodies are far from transparent, and remain unaware of the concerns of civil-rights and social-justice advocates. I caution that even sophisticated predictive systems will not deliver police reform without regulatory and institutional changes. Checks and balances are needed to mitigate police discretionary power. We should be wary of relying on commercial products that Reform predictive policing