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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, 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 to .

Police use of these national security-style surveillance techniques—justified as cost-effective techniques that avoid human bias and error – hand-in-hand with the increased militarization of law enforcement. Extensive research, , has shown that these expansive and powerful surveillance capabilities have exacerbated rather than reduced , in policing, and they pose a growing .

Police reform efforts are increasingly looking at law enforcement organizations' use of surveillance technologies. In the wake of the current unrest, , and 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 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 technologies. In 2009, in the face of federal, state and local caused by the Great Recession, police departments began looking for ways to do more with less. Technology companies rushed to fill the gaps, offering 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 and . 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 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 to racial bias. In large measure, the public relations strategy worked, which has allowed law enforcement to embrace and increased .

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 to adopt a technology-based solution to deep societal problems. Police chiefs are likely to want to turn the page from the current levels of community anger and distrust.

The dangers of high-tech 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 with deep pockets and big ambitions.

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. with hundreds of police departments through its and . Other companies like BriefCam, Palantir and Shotspotter offer a host of , and other 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 and capabilities, turning static surveillance into a to find patterns in crowds. Adding to the data trap are , and , which now allow police to uncover individuals' digital trails with relative ease.

The technology is more interconnected. One of the natural limiting factors of first generation big data policing technology was the fact that it remained siloed. Databases could not communicate with one another. Data could not be easily shared. That limiting factor has shrunk as more aggregated data systems have been developed and .

The promise of objective, unbiased technology didn't pan out. was not fixed by turning on a camera. Instead the technology created new problems, including highlighting the for high-profile instances of police violence.

Lessons for reining in police spying

The harms of big data policing have been repeatedly exposed. Programs that attempted to predict individuals' behaviors in and have been shut down after devastating cataloged their discriminatory impact and practical failure. have been in Los Angeles and other cities that initially had adopted the technology. Scandals involving , and large-scale 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 of big data policing is that issues of race, transparency and constitutional rights must be at the forefront of design, regulation and use. Every can be traced to a failure to see how the surveillance technology fits within the context of modern police power—a context that includes longstanding issues of racism and social control. Every solution points to addressing that power imbalance at , through local oversight, and , not after the technology has been adopted.

The debates about , and existing law enforcement practices must include a discussion about police surveillance. There are a decade of missteps to learn from and era-defining privacy and racial justice challenges ahead. How police departments respond to the siren call of will reveal whether they're on course to repeat the same mistakes.