Flock Safety, a fast-growing U.S.-based surveillance technology company, is under scrutiny following revelations that it relies extensively on overseas gig workers to develop its artificial intelligence systems, according to a report titled “Flock Uses Overseas Gig Workers to Build Its Surveillance AI” published by Startup News FYI. The report raises critical questions about the privacy implications, labor practices, and ethical considerations surrounding the company’s model for building AI-powered public safety tools.
Flock, best known for its automated license plate recognition (ALPR) cameras used by law enforcement agencies across the United States, has rapidly expanded its footprint over the past few years. These devices capture and analyze images of vehicles to aid in investigations ranging from stolen cars to more serious crimes. Flock claims its systems have helped solve thousands of cases, but the Startup News FYI article highlights concerns about what goes into the making of these tools.
At the center of the report is Flock’s reliance on a distributed workforce of gig contractors, many based outside the United States, to manually label the vast troves of images and video data that feed into the company’s machine learning models. According to Startup News FYI, this process includes tagging cars, license plates, makes and models—work that trains the AI to automatically detect such elements in real time. The outsourcing of this sensitive task raises questions about data security, training accuracy, and legal compliance, especially given the privacy-sensitive nature of the information being handled.
Critics argue that the use of low-paid gig workers to label surveillance data in developing countries not only triggers ethical concerns about labor exploitation but also could result in weaker oversight and reduced quality assurance. Furthermore, some experts warn that using non-local workers for such tasks might further disconnect algorithmic development from the communities that are most impacted by the deployment of such technologies.
Flock has defended its practices, suggesting that utilizing a flexible, global gig economy model allows it to scale quickly and manage rising demand from municipal and police partners. The company maintains that it follows strict data-handling protocols, though the Startup News FYI article notes a lack of transparency around what those protocols entail and how they are enforced across jurisdictions.
These revelations add another layer to the broader debate about AI development and surveillance in the private sector, particularly as cities and counties increasingly enter into contracts with technology providers for public safety infrastructure. Questions persist around due diligence, public consent, and the regulatory frameworks necessary to ensure that advanced surveillance tools are deployed responsibly and equitably.
As Flock continues its ascent in the public safety technology space, the scrutiny over its backend operations is likely to grow. The Startup News FYI report underscores the importance of examining not just what AI-powered systems do, but how they are made—and by whom.
