Take a fresh look at your lifestyle.

The Case Against High-Tech Surveillance in Policing

Reports that 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 filled social media feeds while police surveillance of protesters has remained largely out of sight, has become a great concern.

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 one by National Interest’s Andrew Guthrie Ferguson, 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 world of police cameras, smart sensors, and predictive analytics had not always been. Recession and rage-fueled the initial rise of big data policing technologies. In 2009, in the face of US 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.

Instead of repeating the mistakes of the past 12 years or so, communities have an opportunity to reject the expansion of big data policing. The dangers have only increased, the harms made plain by experience.

Those small startup companies that initially rushed into the policing business have been replaced by big technology companies with deep pockets and big ambitions.

Axon capitalized on the demands for police accountability after the protests in Ferguson and Baltimore to become a multimillion-dollar company providing digital services for police-worn body cameras. Amazon has been expanding partnerships with hundreds of police departments through its Ring cameras and Neighbors App. Other companies like BriefCam, Palantir, and Shotspotter offer a host of video analytics, social network analysis, and other sensor technologies with the ability to sell technology cheaply in the short run with the hope for long term market advantage.

The technology itself is more powerful. 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, turning static surveillance into a virtual time machine to find patterns in crowds. Adding to the data trap are smartphones, smart homes, and smart cars, which now allow police to uncover individuals’ digital trails with relative ease.

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 after devastating audits cataloged their discriminatory impact and practical failure. Place-based predictive systems have been shut down in Los Angeles and other cities that initially had adopted the technology. 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.

Leave A Reply

Your email address will not be published.

WhatsApp chat