How Ring & Rekognition Set the Stage for Consumer Generated Mass Surveillance
If every home on a street, in a neighborhood, or in a town had a Ring surveillance system, the individual cameras, taken together, could construct an extremely intimate picture of daily public life. By integrating facial recognition and contracting with local and federal law enforcement agencies, Amazon supercharges the potential for its massive network of surveillant consumers to comprehensively track the movements of individuals over time, even when the individual has not broken any law. Fully realized, these technologies set the stage for consumer generated mass surveillance.
Amazon’s Ring surveillance system dominates the growing video doorbell market. Ring, acquired by Amazon last April, is a system of home surveillance doorbell cameras which operate on an integrated social media platform, Neighbors. Neighbors allows users to share camera footage with other users and law enforcement agencies, as well as report safety issues, strangers, or suspicious activities. The platform aggregates user-generated reports and video data into a local activity maps and watchlists. Similar community platforms where neighbors can report suspicious persons or activity, such as NextDoor, are notorious for racial bias and profiling. This problem will surely be made worse by Amazon’s desire to automatically classify persons as “suspicious” through sentiment analysis and other biometric data collection.
A recent patent application shows that Amazon will integrate their facial recognition product Rekognition into the Ring system, while also collecting and analyzing a litany of other biometric information. Many raise serious concerns about the integration of facial recognition to our contemporary digital ecosystem. Indeed, a unique consensus among researchers, lawmakers, advocates, and technology companies that facial recognition technology amplifies bias, intensifies mass surveillance and ought be subject to stringent regulation. [For more WJLTA coverage on algorithmic bias, see here.]
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