Turing Normalizing Machine

An experiment in machine learning & algorithmic prejudice

The Turing Normalizing Machine is an experimental research in machine-learning that identifies and analyzes the concept of social normalcy. Each participant is presented with a video line up of 4 previously recorded participants and is asked to point out the most normal-looking of the 4. The person selected is examined by the machine and is added to its algorithmically constructed image of normalcy. The kind participant's video is then added as a new entry on the database.
As the database grows the Turing Normalizing Machine develops a more intricate model of normal-appearance, and moves us closer to our research goal: to once-and-for-all decode the mystery of what society deems “normal” and to automate the process for the advancement of science, commerce, security and society at large.

Conducted and presented as a scientific experiment TNM challenges the participants to consider the outrageous proposition of algorithmic prejudice. The responses range from fear and outrage to laughter and ridicule, and finally to the alarming realization that we are set on a path towards wide systemic prejudice ironically initiated by its victim, Turing.

by Yonatan Ben-Simhon & Mushon Zer Aviv
Presented at Bloomfield Science Museum, Jerusalem, 2012