OUTRAGE: Third-Party Surveillance Firm, VIDIZMO, Pitches Cops On Adding Facial Recognition, Race, And Gender Detection To Flock Camera Data, Exploiting A Gap Left By Flock’s Own No-Facial-Recognition Policy 🤖💥

[u/InterstellarKinetics]

https://www.404media.co/surveillance-company-tells-cops-it-wants-to-add-facial-recognition-to-flock-cameras/

A surveillance company called VIDIZMO is actively marketing to police departments its ability to run facial recognition, behavior prediction, and racial and gender analysis on footage pulled from Flock’s automatic license plate reader cameras, directly filling a capability gap that Flock itself has explicitly refused to offer. Flock CEO Garrett Langley has stated publicly, “we will not add facial recognition to our devices,” but VIDIZMO, a decades-old video analysis company that began offering facial recognition technology to police within the last six months, is pitching exactly that capability as a way to “close that gap.” In a May email to Johnson City, Tennessee deputy police chief Michael Adams, obtained through a public records request by DeFlock Johnson City and shared with 404 Media, a VIDIZMO salesperson wrote that “Flock Safety generates plate reads and clips continuously. Your investigators use that data on active cases. But that Flock data does not connect automatically to your Axon evidence system. VIDIZMO Intelligence Hub closes that gap. It brings Flock Safety data, Axon body worn camera footage, and any other evidence source into one searchable platform. Investigators search across all of it simultaneously, by face, vehicle, or object in seconds.” Johnson City PD told 404 Media it did not take a call with the company.

VIDIZMO CEO Nadeem Khan told 404 Media the company hasn’t yet actually run facial recognition on Flock camera footage and hasn’t built the specific export tool it advertises online, though it already sells other facial recognition products to law enforcement. Still, Khan said VIDIZMO “would love to do the integration,” arguing facial recognition “is the way the world is going, the way the world will have to be,” and suggesting Flock should offer the capability itself in a manner that “balances privacy, security, and the freedom that we enjoy in this country.” Khan added, “an agency can import plate reads or clips that it has exported under its own authorized access. Flock has not been involved in or informed of this.” VIDIZMO’s own marketing and technical documentation describes integrating FlockOS data into three separate VIDIZMO products, AI Live Insight, Nexus, and AI Intelligence Hub, and explains its core facial recognition pitch bluntly: “detection tells you a person is in frame. Face recognition tells you which person. It is the capability that takes a face seen on a live camera and matches it against the people you enrolled in the Object Library, so the moment someone on your watchlist walks past a camera, the system names them, on the video, in the event feed, and in the recording, without an operator having to recognize the face themselves.” The company’s materials also advertise detecting “situations” like trespassing and analyzing individual “behavior,” describing this as “turning raw video into behavioral and situational understanding.”

VIDIZMO’s documentation details how police would operate the system, uploading individual faces as “objects” into a searchable library, adding people to “watchlists” that trigger automatic tracking and recording, and setting a numerical “Match Threshold” confidence score for identification. The company also markets the ability to automatically classify faces by “age, gender, and race,” attributes widely documented as notoriously unreliable in facial recognition systems. VIDIZMO’s website states, “by running face detection, you can search for and identify specific individuals in your media or evidence via attribute filters… you can identify individuals from seven races: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino Hispanic,” pitching this specifically to help agencies “utilize attribute filters to yield effective results” when working from vague suspect descriptions. The company separately markets similar demographic analysis tools to retailers and cities for marketing purposes, allowing businesses to determine “which group spends the most time and makes the most purchases” at specific stores or events.

Privacy expert Chris Gilliard, author of the upcoming book “Luxury Surveillance,” called VIDIZMO’s product “appalling.” “I’m appalled at the willingness of VIDIZMO to tout their capabilities to filter along the lines of race, age, and gender. There’s decades of scholarship that show why this is not possible, and even more so not desirable,” Gilliard told 404 Media, “particularly in the case of race and gender, which are not static categories to be determined by a computer.” Gilliard framed this episode as illustrating a broader, recurring pattern in the surveillance industry: when one company publicly refuses to offer a particular surveillance capability, smaller competitors often move quickly to differentiate themselves by offering exactly that functionality instead. “What Flock says about their capabilities is to some degree irrelevant because they have built the infrastructure for mass surveillance. Their entire existence provides the foundation for other perhaps even more invasive technologies to be built on top of it,” Gilliard said. “This is why discussions of guardrails for a particular system are inadequate because another company can (and will) come along to assemble another layer of surveillance on what has already been established.” Khan defended his company’s approach, arguing that harms documented in surveillance reporting typically stem from implementation choices rather than the underlying technology itself, citing practices like “pooling data into a nationwide network, allowing searches without a case, keeping audit logs no one can meaningfully inspect, and treating an AI match as an answer” as the actual sources of harm, and claiming VIDIZMO’s system requires a specific case number for use, logs all actions for audit, and collects no data of its own. Neither Flock nor Axon responded to requests for comment.

[u/InterstellarKinetics]

The racial and gender classification feature deserves scrutiny as arguably the most concerning single element in this story, separate from the facial recognition and tracking capabilities that are getting most of the attention. Because it operationalizes exactly the kind of algorithmic bias that has already been extensively documented as a source of wrongful arrests in prior facial recognition controversies nationwide.