“Training Machines to Detect Suspicious Behaviour” presented by Kronman

The work “Suspicious Behavior” shows a world of hidden human labour, which builds the foundation of how ‘intelligent’ computer vision systems interpret our actions. Through a physical home office set-up and an image labelling tutorial the user traverses into experiencing the tedious work of outsourced annotators. In an interactive tutorial for a fictional company the user is motivated and instructed to take on the task of labelling suspicious behavior. The video clips in the tutorial are taken from various open machine learning datasets for surveillance and action detection. Gradually the tutorial reveals how complex human behavior is reduced into banal categories of anomalous and normal behavior. The guidelines of what is considered suspicious behavior illustrated on a poster series and disciplined in the tutorial exercises are collected from lists of varied authorities. As the user is given a limited time to perform various labelling tasks the artwork provokes to reflect upon how easily biases and prejudices are embedded into machine vision. “Suspicious Behavior” asks if training machines to understand human behavior is actually as much about programming human behavior? What role does the ‘collective intelligence’ of micro tasking annotators play in shaping how machines detect behavior? And in which ways are the world views of developers embedded in the process of meaning making as they frame the annotation tasks?