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 drones.
Police use of these national security-style surveillance techniques – justified as cost-effective and avoiding human bias and error – has grown hand-in-hand with the increased militarization of law enforcement.
But extensive research has shown that these expansive and powerful surveillance capabilities have exacerbated rather than reduced bias, overreach and abuse in policing, and they pose a threat to civil liberties.
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. Companies rushed to fill the gaps, offering new forms of data-driven policing as models of efficiency.
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 police power structure.
In response to cost pressures and community criticism, police departments further embraced startup companies selling big-data efficiencies. Predictive analytics and bodycam video 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.
Today, in the midst of renewed outrage against structural racism and police brutality, and in the shadow of an even deeper economic recession, law enforcement organizations face the same temptation.
Instead of repeating the mistakes, communities have an opportunity to reject the expansion of big-data policing.
Race bias in policing was not fixed by turning on a camera.
Those small startup companies that initially rushed into the policing business have been replaced by big technology companies with deep pockets and big ambitions.
The algorithmic models created a decade ago pale in comparison to machine-learning capabilities today. Video camera streams have been digitized and augmented with analytics and facial-recognition capabilities.
The promise of objective, unbiased technology didn’t pan out. Race bias in policing was not fixed by turning on a camera. Instead, the technology created new problems, including highlighting the lack of accountability for high-profile instances of police violence.
The harms of big-data policing have been repeatedly exposed. Programs that attempted to predict individuals’ behaviors in Chicago and Los Angeles have been shut down. Scandals involving facial recognition, social-network-analysis technology and large-scale sensor surveillance serve as a warning that technology cannot address the deeper issues of race, power and privacy that lie at the heart of modern-day policing.
The lesson of the first era of big-data policing is that issues of race, transparency and constitutional rights must be at the forefront of design, regulation and use. Every solution points to addressing the power imbalance at the front end, through local oversight, community engagement and federal law, not after the technology has been adopted.
The debates about defunding, demilitarizing and reimagining existing law enforcement practices must include a discussion about police surveillance.
Andrew Guthrie Ferguson is a professor of law at American University. Distributed by The Associated Press.
