⇓ More from ICTworks

How to Anonymously Estimate Age for Digital Identity Solutions

By Guest Writer on November 11, 2020

digital age estimation

Yoti spent the last six years building a digital identity platform and we’ve made impressive advancements in the age checking space. Our free app is a powerful tool that lets anyone convert their government-issued ID document into an encrypted, digital ID. Once verified, users can share age details, such as ‘Over 18’ or ‘Under 18’, without revealing their entire date of birth.

Age Verification is a Global Problem

After delivering age verification in this way for a few years, it struck us that in the UK, 33% of people under the age of 18, and 24% of those over 18, do not have photo ID documents – many in lower income households where people do not travel abroad or drive a car.

Across the world, the issues is even greater. There are over 1 billion people with no government-issued or other official photo ID. Hence, one of the Sustainable Development Goals​is is to “provide legal identity to all“. Yoti, in turn, also has a core business principle to make Yoti available to anyone.

Anonymous Age Estimation Program

So in 2017, our research and development team started looking into using machine learning to build a system that could recognise the age of a face. Using neural networks, we discovered we could teach a machine to use just an image to identify gender and a year of birth.

From the beginning, we made a commitment to transparency, bias minimisation and ethical usage, underlined by our signing of the Safe Face Pledge​.

  • To minimise bias, we are fortunate to benefit from the diverse global Yoti community members that add ID documents. We publish the results, across skin tones, ages and genders in our regularly-updated white paper​.
  • For ethical use, we recognised that some Yoti users do not wish to take part in this research and an opt-out route is clearly provided.

In addition, we’ve hosted roundtable sessions​ to get input from civil society and also invited the Centre for Democracy and Technology to do a deep dive into the technology. We also worked with Dr Allison Gardner, an IEEE expert​, to do an Algorithmic Bias review, to ensure we were developing the algorithm aiming to minimise bias and be transparent about the levels of accuracy and bias.

Yoti Age Estimation Accuracy Rates

Over the last two years, we’ve trained the system with an image, gender and year of birth with over 300 million age estimates. These are our latest October 2020 algorithm results:

digital age estimation

The system is now able to judge age within a couple of years. For 16-25 year olds, it can usually estimate the age within 1-2 years. In contrast, humans guess age within about 4-8 years of accuracy​.

How We Are Protecting People Online

The age estimation tool makes life easier for people who want to limit how much personal information they share online. It is fully anonymous and there is no way of linking a person’s face with an identity. All images are instantly deleted and an estimated age is delivered in a matter of seconds.

The system also makes it easier for businesses to safeguard users and ask for less data. With the growing popularity of social media and live streaming platforms, safeguarding children is becoming a number one safety priority and this is a seamless tool that allows companies to do that.

Yubo and GoBubble are just two of many partners that are keeping their online communities safe with this tool. With huge user bases spread across the world, our scalable system allows such companies to review many millions of users and deter people lying about their age. It also allows for companies to obtain parental consent which is given by an adult through estimated age.

What’s more, the tool can be used for online and offline retail settings, for privacy-preserving adult content age verification, or for gambling or dating site age checks. For example, we were asked to take part in a BBC documentary #NudesForSale​, where they used it to check how many underage users were using a platform for over 18s.

As part of the Yoti principles, we make our age estimation tool available for free for registered NGOs and other social change organisations at our discretion. For example, we made the tool available to help ascertain the age of victims and perpetrators in child sexual abuse material.

Next Steps in Algorithmic Age Estimation

From Q4 2020, we’ll be working with several safeguarding bodies to consider widening our research, support companies looking to comply with the Age Appropriate Design Code, and help improve accuracy with young people. Our aim is to:

  1. Extend the age range to also include younger people aged between 7-12.
  2. Further improve the accuracy by using month as well as year-of-birth. This will be tested over the coming months.

Please leave a comment if you want to ask any further questions about our age estimation or our other R&D work​.

By Julie Dawson, Director of Regulatory and Policy at Yoti and originally published as Developing our anonymous age estimation technology

Filed Under: Solutions
More About: , , , , ,

Written by
This Guest Post is an ICTworks community knowledge-sharing effort. We actively solicit original content and search for and re-publish quality ICT-related posts we find online. Please suggest a post (even your own) to add to our collective insight.
Stay Current with ICTworksGet Regular Updates via Email

Leave a Reply

*

*