Help Your Child See How Algorithms See Them
Ever wonder what determines what your teenager sees on TikTok, YouTube, or Netflix?
The Synthetic Society Lab and Oxford Child-centred AI Lab are researching how algorithms create hidden profiles of young people based on their online activity. We've developed Algorithmic Mirror —an application built on Latent Lab that reveals how platforms might categorise your child's digital footprint.
As one teen from a recent study said: "It's like there's an FBI agent watching us... I want to know what they're doing with our data."
Join our study and:
- See unique visualisations showing how platforms might categorise their interests over time
- Understand how their viewing habits data can be analysed by algorithms
- Learn practical strategies for managing their digital footprint
Who: Families with teens ages 11-17
What: A research session exploring algorithmic categorisation
Why: To give teen insight into the invisible forces shaping young people's digital experiences
Help your child understand the digital world they navigate every day.
Prototype: Algorithmic Mirror
Preprint: https://arxiv.org/abs/2504.16615
Lead Researcher: Yui Kondo (Research Associate, Oxford Internet Institute)
Principal Investigator: Dr.Luc Rocher (Associate Professor, Oxford Internet Institute, University of Oxford, UK)
Collaborator:
Kevin Dunnell, (PhD candidate at MIT Media Lab, USA),
Qing Xiao (PhD Candidate at Carnegie Mellon University, USA)
Dr. Jun Zhao (Senior Researcher,the Oxford Child-Centred AI Lab of Oxford Computer Science Department, University of Oxford)
Why does this matter?
Every video watched, every like, and every interaction creates data that platforms use to build sophisticated profiles of your child. These algorithmic profiles determine:
vulputate
- Content Curation: What content appears in their feeds
- Time Spent: How much time they spend online
- Targeted Ads: Which advertisements target them
- Behaviour Prediction: How their interests and behaviours are predicted and influenced
The good news: When young people understand how algorithms interpret their data, they become more thoughtful digital citizens who can better protect their privacy and wellbeing online.Introducing Algorithmic Mirror
The Synthetic Society Lab at Oxford Internet Institute, in collaboration with MIT Media Lab, Carnegie Mellon University, and the Oxford Child-centred AI Lab
, has developed Algorithmic Mirror—an innovative visualisation tool that reveals how platforms like YouTube, TikTok, and Netflix interpret young people's viewing histories.
Your Child will:
“With Algorithmic Mirror, I realize I'm watching quite a bit of content from genres that don't even remain in my memory. It's scary to realize that I'm watching things without awareness.”
Your Child will:
- See unique visualisations showing how algorithms categorise their interests over time
-
Understand how their view habits data can be analysed by algorithms
- Learn practical strategies for managing their digital footprint
- Develop critical thinking skills about algorithmic systems
“With Algorithmic Mirror, I realize I'm watching quite a bit of content from genres that don't even remain in my memory. It's scary to realize that I'm watching things without awareness.”
FROM A PARTICIPANT IN THE LAST STUDY
Temporal Evolution of Digital Footprint
Our participants from last study realised that the categories and generative summaries were based on long-term data, and observed,
“recommendation algorithms remember you, like how you were interested.''
Another participant critically questioned,
“ When YouTube tries to understand what I like, my question is how would it [recommender system] try to track my interest over time and project new interests, or would it just like take me as I currently am and give me exactly what I like? [...] I can imagine it can try to predict how my personality would evolve in the next five years or so [...] it's a little scary”
Our participants from last study realised that the categories and generative summaries were based on long-term data, and observed,
“recommendation algorithms remember you, like how you were interested.''
Another participant critically questioned,
“ When YouTube tries to understand what I like, my question is how would it [recommender system] try to track my interest over time and project new interests, or would it just like take me as I currently am and give me exactly what I like? [...] I can imagine it can try to predict how my personality would evolve in the next five years or so [...] it's a little scary”
Who can participate?
We're seeking:
- Young people aged 11-17 years
- Who actively use at least one social media platform (YouTube, Netflix, or TikTok)
- Willing to participate either in person in Oxford or online
What's involved?
Session 1: Data Download (30 minutes)
- Virtual guidance session to download platform data
- Conducted from home or school
- Complete privacy—no data shared with researchers
Session 2: Interactive Workshop (60-90 minutes)
- Secure data upload to Algorithmic Mirror
- Explore personalised algorithm visualisations
- Group discussions with peers about findings
- Parents welcome to observe
Optional: Follow-up Interview (30 minutes)
- Share experiences and insights
- Help improve the tool for future users
Your child's safety is paramount
- Full ethics approval from University of Oxford (Reference: [1990161])
- Password-protected visualisations only accessible to your child
- Experienced researchers with safeguarding training
- All personal information removed before analysis
- Automatic data deletion after 6 months
Benefits for participants
We hope this study will help participants learn more about algorithms on social media and help your child become a more informed and empowered digital citizen, with potential benefits for other children in the future.
You will receive £20 total as our thanks for participating: £10 when you upload your data to the algorithmic mirror, and another £10 upon completing the workshop.