Social media platforms routinely track and monetize adolescents' data while offering little visibility into how algorithms construct their digital identities. While XAI scholars have sought to reveal the inner workings of algorithms, their approaches frequently overwhelm end-users with technical complexity. We introduce Algorithmic Mirror, an interactive visualization tool that transforms opaque profiling practices into explorable landscapes of personal data. It uniquely leverages adolescents' real digital footprints across TikTok, YouTube, and Netflix to provide situated, personalized insights into datafication over time. In our study with 27 participants aged 12 to 16 we show how engaging with their own data enabled adolescents to uncover the scale and persistence of data collection, recognize cross-platform profiling, and critically reflect on algorithmic categorizations of their interests. Our findings show that identity is a powerful motivator for reflecting on the impact of algorithms, highlighting the need for platforms to ensure young people have transparency and control over their algorithmic profiles.

Proceedings of the CHI 2026 Workshop on Human-Centered Explainable AI (HCXAI); April 13–17, 2026; Barcelona, Spain.