Algorithmic Mirror
Helping adolescents see and shape their algorithmic selves
Role First author
Year 2024- (Ongoing)
Link https://www.media.mit.edu/projects/algorithmic-mirror/overview/
Key words algorithmic awareness, adolescents, social media
Rather than offering dashboards of quantitative metrics or metadata counts, Algorithmic Mirror generates an interactive, explorable landscape: a speculative “profile” of young people’s digital traces. The project does not uncover actual platform models but instead constructs possible mirrors of algorithmic judgment—artificial reflections that expose how identity could be abstracted and rearranged through data.
By turning opaque inferences into interactive spatial metaphors, the tool invites reflection on how children’s online footprints may be silently transformed into profiles, and how inhabiting such mirrors might alter one’s sense of self.
“When YouTube tries to understand what I like, my question is how would it 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?” - P9
User could see their YouTube self, TikTok self, and Netflix self in a unified explorable landscape.
“It made me reconsider how my interests are distributed across platforms. It's as if different personalities exist within each platform” P2
Credits
Yui Kondo, Oxford Internet Institute
Kevin Duneell, MIT Media Lab
Isobel Voysey, CS@Oxford
Qing Hu, HCII@CMU
Victoria Paesano, CS@MIT
Phi Nguyen, CS@CMU
Dr. Jun Zhao @Oxford Child-centred AI Lab
Dr. Luc Rocher @Oxford Synthetic Society