(Un)predictable acts of data in machine learning environments

Abstract

This paper investigates artistic representations of machine learning and their interventional potential. Taking its point of departure in two works of art, the paper discusses effects of predictability and unpredictability caused by machine learning systems. By thinking through “eventfulness” (Bucher) and “nonconscious cognition” (Hayles) in human and non-human environments, the paper analyzes the potential of artistic practices to question and rethink algorithmic processing. The paper provides a framework in which artwork challenges forms of technological predictability and comes to terms with machine learning as a fundamental cultural practice in its own right.