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Artificial Intelligence

YouTube to introduce Digital ID Age Checks and AI Profiling

Published

2 months ago

4 minute read

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By Ken Macon

YouTube will soon be a gated community: no ID, no login.

If you’re tired of censorship and surveillance, subscribe to Reclaim The Net.

Australia is preparing to prohibit children under 16 from holding social media accounts by the end of the year, and YouTube will now be included among the platforms required to comply. This will require the roll out of digital ID checks.

More: The Digital ID and Online Age Verification Agenda

At the same time, in the United States, YouTube has begun deploying artificial intelligence tools that estimate users’ ages in an effort to impose teen-specific protections automatically, regardless of the birthdate users provide when signing up.

This new system, based on machine learning, examines a range of user signals such as viewing history and account behavior to infer age. If a user is likely to be a teenager, YouTube will adjust their experience by turning off personalized advertising, activating screen time reminders, and limiting the repeated viewing of videos that may contribute to negative body image or social hostility.

These safety features already exist for users who have confirmed they are under 18. The current change allows YouTube to enforce them even for those who have not disclosed their actual age.

In cases where someone over 18 is misidentified, they will have the option to verify their age by submitting a government ID, credit card, or selfie. Only users who are confirmed adults or inferred to be over 18 will be permitted to view age-restricted material.

The technology will roll out to a small group of US users over the coming weeks, with broader deployment expected after performance reviews. YouTube announced its plans for age-estimation features in February as part of its 2025 roadmap. This follows earlier youth safety initiatives, including the YouTube Kids app and, more recently, supervised accounts.

Although YouTube has not revealed all the data points used by its system, the company has stated that it will evaluate things like account longevity and platform activity. The age-estimation process will apply only to users who are signed in. Those browsing the site without logging in are already blocked from viewing certain content. The new protections will apply across all platforms, including desktop, mobile, and smart TVs.

Back in Australia, YouTube’s status has shifted significantly. After initially being granted an exemption from the national under-16 social media ban, the platform is now being brought under the same new rules as TikTok, Instagram, Snapchat, and others. The reversal follows advice from the pro-censorship eSafety commissioner, who raised concerns about YouTube.

“The Albanese government is giving kids a reprieve from the persuasive and pervasive pull of social media while giving parents peace of mind,” said Communications Minister Anika Wells. “There’s a place for social media, but there’s not a place for predatory algorithms targeting children.”

The more curated YouTube Kids app will remain unaffected by the restrictions, but the main platform will be included in the ban beginning December 10.

If you’re tired of censorship and surveillance, subscribe to Reclaim The Net.

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Artificial Intelligence

The App That Pays You to Give Away Your Voice

Published on September 29, 2025

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By Ken Macon

What sounds like side hustle money is really a permanent trade of privacy for pennies

An app that pays users for access to their phone call audio has surged to the top of Apple’s US App Store rankings, reflecting a growing willingness to exchange personal privacy for small financial rewards.
Neon Mobile, which now ranks second in the Social Networking category, invites users to record their calls in exchange for cash.
Those recordings are then sold to companies building artificial intelligence systems.
The pitch is framed as a way to earn extra income, with Neon promising “hundreds or even thousands of dollars per year” to those who opt in.
The business model is straightforward. Users are paid 30 cents per minute when they call other Neon users, and they can earn up to $30 a day for calls made to non-users.
Referral bonuses are also on offer. Appfigures, a platform that tracks app performance, reported that Neon was ranked No. 476 in its category on September 18.
Within days, it had entered the top 10 and eventually reached the No. 2 position for social apps. On the overall charts, it climbed as high as sixth place.
Neon’s terms confirm that it records both incoming and outgoing calls. The company says it only captures the user’s side of a conversation unless both participants are using the app.
These recordings are then sold to AI firms to assist in developing and refining machine learning systems, according to the company’s own policies.
What’s being offered is not just a phone call service. It’s a pipeline for training AI with real human voices, and users are being asked to provide this data willingly. The high ranking of the app suggests that some are comfortable giving up personal conversations in return for small daily payouts.
However, beneath the simple interface is a license agreement that gives Neon sweeping control over any recording submitted through the app. It reads:
“Worldwide, exclusive, irrevocable, transferable, royalty-free, fully paid right and license (with the right to sublicense through multiple tiers) to sell, use, host, store, transfer, publicly display, publicly perform (including by means of a digital audio transmission), communicate to the public, reproduce, modify for the purpose of formatting for display, create derivative works as authorized in these Terms, and distribute your Recordings, in whole or in part, in any media formats and through any media channels, in each instance whether now known or hereafter developed.”
This gives the company broad latitude to share, edit, sell, and repurpose user recordings in virtually any way, through any medium, with no expiration or limitations on scope.
Users maintain copyright over their recordings, but that ownership is heavily constrained by the licensing terms.
Although Neon claims to remove names, phone numbers, and email addresses before selling recordings, it does not reveal which companies receive the data or how it might be used after the fact.
The risks go beyond marketing or analytics. Audio recordings could potentially be used for impersonation, scam calls, or to build synthetic voices that mimic real people.
The app presents itself as an easy way to turn conversations into cash, but what it truly trades on is access to personal voice data. That trade-off may seem harmless at first, yet it opens the door to long-term consequences few users are likely to fully consider.
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Artificial Intelligence

UK Police Chief Hails Facial Recognition, Outlines Drone and AI Policing Plans

Published on September 16, 2025

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By Ken Macon

Any face in the crowd can be caught in the dragnet of a digital police state.

The steady spread of facial recognition technology onto Britain’s streets is drawing alarm from those who see it as a step toward mass surveillance, even as police leaders celebrate it as a powerful new weapon against crime.
Live Facial Recognition (LFR) is a system that scans people’s faces in public spaces and compares them against watchlists.
Civil liberties groups warn it normalizes biometric monitoring of ordinary citizens, while the Metropolitan Police insist it is already producing results.
Britain’s senior police leadership is promoting these biometric and artificial intelligence systems as central to the future of policing, with commissioner Sir Mark Rowley arguing that such tools are already transforming the way the Met operates.
Speaking to the TechUK trade association, Rowley described Live Facial Recognition (LFR) as a “game-changing tool” and pointed to more than 700 arrests linked to its use so far this year.
Camera vans stationed on streets have been deployed to flag people wanted for serious crimes or those breaking license conditions.
Rowley highlighted a recent deployment at the Notting Hill Carnival, where he joined officers using LFR.
“Every officer I spoke to was energized by the potential,” he said to The Sun. According to the commissioner, the weekend brought 61 arrests, including individuals sought in cases of serious violence and offenses against women and girls.
Rowley claimed that the technology played “a critical role” in making the carnival safer.
Beyond facial recognition, Rowley spoke of expanding the Met’s reliance on drones. “From searching for missing people, to arriving quickly at serious traffic incidents, or replacing the expensive and noisy helicopter at large public events,” he said, “done well, drones will be another tool to help officers make faster, more informed decisions on the ground.”
The commissioner also promoted the V100 program, which draws on data analysis to focus resources on those considered the highest risk to women.
He said this initiative has already led to the conviction of more than 160 offenders he described as “the most prolific and predatory” in London.
Artificial Intelligence is being tested in other areas too, particularly to review CCTV footage.
Rowley noted the labour involved in manually tracing suspects through crowded areas. “Take Oxford Street, with 27 junctions—a trawl to identify a suspect’s route can take two days,” he explained.
“Now imagine telling AI to find clips of a male wearing a red baseball cap between X and Y hours, and getting results in hours. That’s game-changing.”
While the Met portrays these systems as advances in crime prevention, their deployment raises questions about surveillance creeping deeper into everyday life.
Expansions in facial recognition, drone monitoring, and algorithmic analysis are often introduced as matters of efficiency and safety, but they risk building an infrastructure of constant observation where privacy rights are gradually eroded.
Shaun Thompson’s case has already been cited by campaigners as evidence of the risks that come with rolling out facial recognition on public streets.
He was mistakenly identified by the technology, stopped, and treated as though he were a wanted suspect before the error was realized.
Incidents like this highlight the danger of false matches and the lack of safeguards around biometric surveillance.
For ordinary people, the impact is clear: even if you have done nothing wrong, you can still find yourself pulled into a system that treats you as guilty first and asks questions later.
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