Online trackers are moving into the physical realm. The judgement of a user’s likes and search queries is replicated offline: by analysing an individual for demographic features and following them around, using computer vision and wifi+ location tracking. This project aims to give its users awareness of these practices, and to empower them to trick and subvert these systems.

Spieg consists of a mirror which reflects not only the user’s outside appearance, but also their appearance as interpreted by a machine. The mirror displays various demographic and private features of the viewer ­ such as age, gender, mood and how often they visit the area – determined with computer vision and wifi tracking. These ‘facts’ are accompanied by imagery of products that are typically advertised to the user in their category. Combined, this distorted version of the viewer’s identity shows both their assigned labels and the resulting conclusions concerning their lifestyles.

Just like we use mirrors to dress in a way that we want others to see us, we can use this mirror to control the way in which machines see us. By wearing special glasses which hide the user’s face from facial recognition, or by turning off their wifi, or by simply changing their hair and make­up, users can see their machine reflections change and their natural face re-emerge. Users can leave the house expressing their chosen identity to both other people and to the machines.

Written in Python and OpenGL