{"source":{"name":"Evidence Hub on Social Media Ban for Kids - A project by the Lisbon Council","url":"https:\/\/socialmediaban.lisboncouncil.net","license":"Creative Common CC-BY 4.0 International"},"data":[{"data":[50,28.6,80,42.9,33.3,25,30,33.3,25,20],"name":"Age verification (some cases)"},{"data":["","","","","","",10,"",25,""],"name":"Age estimation (all cases)"},{"data":[42.9,28.6,20,28.6,"",25,10,16.7,25,20],"name":"Age estimation (some cases)"},{"data":[7.1,42.9,"",14.3,66.7,50,40,50,25,20],"name":"Self-declaration only"},{"data":["","","",14.3,"","",10,"","",40],"name":"Neither assurance nor self-declaration"}],"_data":[["Service Category","Age verification (some cases)","Age estimation (all cases)","Age estimation (some cases)","Self-declaration only","Neither assurance nor self-declaration"],["Social media",50,"",42.9,7.1,""],["Random live video chat",28.6,"",28.6,42.9,""],["Pornography",80,"",20,"",""],["Messaging",42.9,"",28.6,14.3,14.3],["Immersive environments",33.3,"","",66.7,""],["Generative AI",25,"",25,50,""],["Gaming",30,10,10,40,10],["For kids",33.3,"",16.7,50,""],["Dating",25,25,25,25,""],["App stores",20,"",20,20,40]],"labels":{"values":["Social media","Random live video chat","Pornography","Messaging","Immersive environments","Generative AI","Gaming","For kids","Dating","App stores"]},"metadata":{"link":"https:\/\/www.oecd.org\/en\/publications\/age-assurance-practices-of-50-online-services-used-by-children_a19853ab-en.html","type":"","unit":"Percent (%)","year":"2024","title":"Prevalence of Age Assurance Mechanisms Among Online Services (2024)","topic":"Usage Patterns","method":"data collection","source":"OECD, Age assurance practices of 50 online services used by children","sub_topic":"","chart_number":"84.0","geographical":"World"},"description":"Based on 50 digital service instances. Please note that services may employ more than one mechanism, so the totals represent the frequency of use across the sample.\r\n\r\nThis chart illustrates the prevalence and systematic nature of age assurance mechanisms across 50 online services in 2024. The data represent the percentage usage within each category. The data reveal that 'age verification in some cases' (conditional\/situational checks) is the most widely used technical strategy. This approach is dominant in the pornography sector (80%) and social media (50%), suggesting that these high-risk areas rely heavily on reactive verification triggers.\r\nA significant trend is the continued reliance on the 'honesty box' model: 'Self-declaration only', in fact, remains a primary tool for several sectors, accounting for 66.7% of immersive environments and 50% of both generative AI and 'for kids' services. Additionally, 'age estimation in some cases' serves as a frequent situational layer, particularly for social media (42.9%) and random live video chat (28.6%).\r\nThe data also highlight the extreme rarity of systematic barriers across the entire user base. 'Age estimation in all cases' is used by only two sectors: Dating (25%) and Gaming (10%). Furthermore, the chart reveals a significant enforcement gap in the App Store category, which has the highest rate of total non-compliance, with 40% of services providing neither age verification mechanisms nor self-declaration prompts. Overall, these findings emphasise that technical interventions are currently used as discretionary filters rather than as universal entry requirements, even in services designed specifically for children or containing high-risk content."}