SurveillanceHigh-Tech Surveillance Amplifies Police Bias and Overreach
Local, state and federal law enforcement organizations use an array of surveillance technologies to identify and track protesters, from facial recognition to military-grade drones. Police use of these national security-style surveillance techniques – justified as cost-effective techniques that avoid human bias and error – has grown hand-in-hand with the increased militarization of law enforcement. Extensive research, including my own, has shown that these expansive and powerful surveillance capabilities have exacerbated rather than reduced bias, overreach and abuse in policing, and they pose a growing threat to civil liberties.
Video of police in riot gear clashing with unarmed protesters in the wake of the killing of George Floyd by Minneapolis police officer Derek Chauvin has filled social media feeds. Meanwhile, police surveillance of protesters has remained largely out of sight.
Local, state and federal law enforcement organizations use an array of surveillance technologies to identify and track protesters, from facial recognition to military-grade drones.
Police use of these national security-style surveillance techniques – justified as cost-effective techniques that avoid human bias and error – has grown hand-in-hand with the increased militarization of law enforcement. Extensive research, including my own, has shown that these expansive and powerful surveillance capabilities have exacerbated rather than reduced bias, overreach and abuse in policing, and they pose a growing threat to civil liberties.
Police reform efforts are increasingly looking at law enforcement organizations’ use of surveillance technologies. In the wake of the current unrest, IBM, Amazon and Microsoft have put the brakes on police use of the companies’ facial recognition technology. And police reform bills submitted by the Democrats in the U.S. House of Representatives call for regulating police use of facial recognition systems.
A Decade of Big Data Policing
We haven’t always lived in a world of police cameras, smart sensors and predictive analytics. Recession and rage fueled the initial rise of big data policing technologies. In 2009, in the face of federal, state and local budget cuts caused by the Great Recession, police departments began looking for ways to do more with less. Technology companies rushed to fill the gaps, offering new forms of data-driven policing as models of efficiency and cost reduction.
Then, in 2014, the police killing of Michael Brown in Ferguson, Missouri, upended already fraying police and community relationships. The killings of Michael Brown, Eric Garner, Philando Castile, Tamir Rice, Walter Scott, Sandra Bland, Freddie Gray and George Floyd all sparked nationwide protests and calls for racial justice and police reform. Policing was driven into crisis mode as community outrage threatened to delegitimize the existing police power structure.
In response to the twin threats of cost pressures and community criticism, police departments further embraced startup technology companies selling big data efficiencies and the hope that something “data-driven” would allow communities to move beyond the all-too-human problems of policing. Predictive analytics and bodycam video capabilities were sold as objective solutions to racial bias. In large measure, the public relations strategy worked, which has allowed law enforcement to embrace predictive policing and increased digital surveillance.