Accepted Paper:

Making machine learn - ethnographic insights on learning algorithms in the field of robotics  


Arne Maibaum (Technical University of Berlin)

Paper short abstract:

Machine learning algorithms claim to learn from raw data. I show, following a field observation in a robotic lab, how algorithms instead are made learning by tinkering and ask about the consequences for their application.

Paper long abstract:

Drawing from ethnographic field studies in robotic labs I want to tackle the addressed question how machines are (made) learning. Following the implementation of a machine learning algorithm into the software of a robot that is prepared for a robotic competition my presentation will show the underlying effort of the robotic scientists that is needed for the algorithm to function. The involved tinkering of the learning input and the algorithm itself deconstructs the myth that surrounds the hype of machine learning partially.

Going from there I want illustrated how specific forms of knowledge (here the expertise of robotic engineers) are inscribed into the algorithms of robots and what consequences a thereby biased algorithm has for contexts of robotic application like security, surveillance or elderly care.

Panel F01
Machine learning, social learning