Around the world, regulators are introducing online safety and age checking laws to protect minors online. But some people believe that age assurance can’t be done without risking our privacy.
In reality, the challenge of checking someone’s age online in a privacy-preserving way has been solved.
In this paper, we address some of the common concerns around age assurance.
Articles March 27, 2026
In this blog series, our CEO Robin Tombs will be sharing his experience, whilst focusing on major themes, news and issues in the world of identity verification and age assurance. This month, Robin speaks about digital IDs, including the importance of choice and how they can make life increasingly difficult for fraudsters. But before getting into the rest of this piece, it’s important to first address the recent fine issued on Yoti by the Spanish regulator AEPD. Spanish regulator AEPD fining Yoti A growing number of individuals and organisations are now aware that Yoti has been fined €950,000
Articles February 24, 2026
In this blog series, our CEO Robin Tombs will be sharing his experience, whilst focusing on major themes, news and issues in the world of identity verification and age assurance. This month, Robin chats about independent testing for accurate facial age estimation, using digital IDs for age-restricted purchases in store and the UK government’s digital ID wallet. Independent testing shows who truly meets the bar In February 2024, Ofcom published guidance that, for an age assurance process to be highly effective at age-gating children in practice, service providers should ensure the process meets 4 criteria: technically accurate, robust,
Articles February 5, 2026
We are delighted to share our latest evaluation by the National Institute of Standards and Technology (NIST) for our newest facial age estimation model. We have seen notable performance improvements across a number of metrics. NIST’s evaluation is extremely thorough – they have over 20 million images for evaluation – and NIST develops their testing methodology over time. This helps to highlight models that are robust across multiple datasets and scenarios. Our strategy for developing our model is not “data dependent”. Machine learning models can benefit greatly from quantity of data at the initial stage, but once reaching maturity,
