Supporting adolescents to challenge algorithmic profiling in online platforms
Social media platforms radically transform how adolescents portray themselves and connect with each other, often at the cost of their privacy and autonomy. We built and tested an interactive visualization tool to understand platforms' opaque profiling practices.
Authors
Yui Kondo , Luc Rocher , and colleagues from Oxford Child-centred AI Lab, MIT Media Lab, and Carnegie Mellon University
Published
2025
Social media platforms regularly track, aggregate, and monetize adolescents’ data, yet provide them with little visibility or agency over how algorithms construct their digital identities and make inferences about them.
We built Algorithmic Mirror, an interactive visualization tool that transforms opaque profiling practices into explorable landscapes of personal data. It uses adolescents’ digital footprints across YouTube, TikTok, and Netflix, to provide situated, personalised insights into datafication over time.
We conducted a study with 27 participants aged 12 to 16. We showed how engaging with their own data enabled adolescents to uncover the scale and persistence of data collection, recognize cross-platform profiling, and critically reflect algorithmic categorizations of their interests.
We find that identity is a powerful motivator for adolescents’ desire for greater digital agency, underscoring the need for platforms and policymakers to move toward structural reforms that guarantee children better transparency and the agency to influence their online experiences.
This project brings together researchers from the Synthetic Society Lab, the Oxford Child-centred AI Lab, MIT Media Lab, and Carnegie Mellon University.