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Amid wave of laws for online safety of children, age-checking tech comes of age
AI-powered age assurance tools reduce error rates to as low as 1.04 years for teens, enabling governments to enforce stricter online age restrictions amid child safety concerns.
- Three months after Australia launched its teen social media ban, regulators are pushing new age-checking rules for social networks, AI chatbots and porn purveyors.
- Child-Safety advocates and regulators say rising abuse, teen mental health worries and AI-generated child sexual images have driven lawmakers to act, while recent AI advances boosted vendors' age-assurance tools and cut costs.
- Yoti reports its latest face analysis model out in April has an average error of 1.04 years, with 'If you look young, you can be challenged, and you may have to provide your ID', said Robin Tombs.
- Platforms have already locked millions of suspected underage accounts, including 550,000 by Meta and 415,000 by Snapchat, but the 10 social media companies in Australia's teen ban withheld effectiveness data.
- Amid global interest, European leaders are coordinating with Australia on verification approaches, while Iain Corby, AVPA executive director, warns some firms did the bare minimum or reduced robust checks.
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7 Articles
7 Articles
Amid wave of kids' online safety laws, age-checking tech comes of age
For years, tech companies successfully resisted pressure from child safety advocates to do more to keep kids off their services, claiming technical limitations would make any attempt to restrict access for teens impractical, overly broad or a...
Amid wave of kids’ online safety laws, age-checking tech comes of age
For years, tech companies successfully resisted pressure from child safety advocates to do more to keep kids off their services, claiming technical limitations would make any attempt to restrict access for teens impractical, overly broad or a security risk.
·United Kingdom
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Total News Sources7
Leaning Left1Leaning Right4Center2Last UpdatedBias Distribution57% Right
Bias Distribution
- 57% of the sources lean Right
57% Right
14%
C 29%
R 57%
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