Articles
Understanding age assurance in Spain's new online safety law
As digital technology continues to shape how people interact, communicate and consume content, protecting children online has become an increasingly urgent issue. Recognising this, the Spanish government has proposed the Organic Law for the Protection of Minors in Digital Environments. The law is now in its final stages of approval. While comparable initiatives such as the UK’s Online Safety Act and California’s Age-Appropriate Design Code exist in other jurisdictions, the Spanish law stands out for its broad scope and emphasis on enforceable age assurance, platform accountability and digital literacy. Its comprehensive framework places it among the leading examples of
"We need an army of Elliots" - why it’s bonkers we’re not using facial age estimation to sell alcohol
Let’s just get this out there: humans are not great at guessing ages. Don’t just take our word for it. Studies have proven this to be the case. Most of us reckon we can largely say if someone is under 25 using the Challenge 25 technique but when put to the test, the truth comes out: retailers do let some under 18s buy alcohol. Not always and not everyone, but some people are incorrectly estimated to be older than they really are. Let’s be honest, this is not ideal. Now, to be fair, not all humans are created equal.
Why facial age estimation, the most accurate age checking tool, shouldn’t be left on the sidelines
Many of us have been there: standing at a self-checkout, scanning our shopping, only to hit a roadblock when the till flags an age-restricted item like a bottle of wine or a pack of beer. With age verification accounting for between 40 – 50% of interventions at self-checkouts, it significantly disrupts and slows down the checkout experience. We wait for a retail worker to approve the sale. The retail worker does a visual estimation of our age – they look at our face and guess whether we’re old enough to buy the item. Most retailers follow the Challenge 25
Why testing data is as important as training data for machine learning models
When developing machine learning systems for facial age estimation, the conversation often centres on the training data: how much you have, how diverse it is, how inclusive it is, and how well it represents your end users. Not to mention, where the data comes from. Intuitively, that focus makes sense. More data presumably leads to better models. But test data is just as important, and in some ways, even more critical for ensuring models perform effectively. Training data: more isn’t always better Common sense would suggest that for a machine learning model “the more data, the better.” And that’s
Texas App Store Accountability Act: what it means for age assurance worldwide
The State of Texas has passed a landmark law – the App Store Accountability Act – that places legal responsibility for age checking squarely on app store operators. Utah was the first state to enact this type of legislation, now followed by Texas. This new regulatory shift has far-reaching implications for digital safety, privacy and innovation around the world. As an age assurance provider, we believe it’s critical to explain the significance of this development, highlight the practical challenges it raises, and offer a path forward that protects both users and platforms. One of the main weaknesses of this
Thoughts from our CEO
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 the UK’s supermarket trials, the growing momentum behind digital ID and developments in online safety regulations. Facial age estimation and Digital ID supermarket trials Many Yoti followers will know we developed effective facial age estimation at the end of 2018 – over 6 years ago. After several UK supermarkets pleaded for the Home Office to trial the technology at self-checkouts, trials were completed